Robot apparatus and behavior deciding method

ABSTRACT

A behavior decision system ( 70 ) includes a perceptual information acquisition unit ( 90 ) which acquires a cause factor being external or internal information acquired by a CCD camera ( 20 ), distance sensor ( 22 ), microphone ( 23 ) or the like and which influences a behavior and a motivational information acquisition unit ( 81 ) which acquires an occurrence tendency of a behavior influenced by the cause factor based on the cause factor from the perceptual information acquisition unit ( 90 ), a behavior selecting processor ( 82 ) which compares occurrence tendencies corresponding to two or more behaviors, acquired by the perceptual information acquisition unit ( 90 ) and motivational information acquisition unit ( 81 ) and belonging to the same group, to thereby select one of the behaviors, and an output semantics converter module ( 68 ) which controls moving parts based on the behavior selected by the behavior selecting processor ( 82 ) for expressing the selected behavior. With the behavior decision system, there can be provided a robot having an improved likeness to a living thing or a creature and showing a more similar behavior to that of an animal.

TECHNICAL FIELD

The present invention generally relates to a robot apparatus behaviordeciding method and a robot apparatus, and more particularly to anautonomous robot apparatus and a method for deciding the behavior of therobot apparatus.

BACKGROUND ART

Recently, there have been proposed robot apparatuses shaped each like ananimal, namely, so-called pet robots. Each of such robot apparatuses isshaped like a dog or cat kept in a common family, and autonomouslybehaves in response to the action such as “hitting” or “patting” by theuser (owner) and adaptively to its surrounding environment. For example,its autonomous behavior includes “yelping”, “mewing”, “sleeping”, etc.similar to the behavior of an actual animal.

If a robot apparatus could behave more similarly to an actual petanimal, it has an improved likeness to a living thing or a creature andthe user will feel more familiar and satisfactory with such a robotapparatus. The robot apparatus will amuse the user thereof more thanever.

To make the robot apparatus behave like an actual animal, it has beenproposed to use an ethological approach for decision of the robotapparatus behavior.

For example, as a result of the behavior study with the ethologicalapproach, a state of motivation space representation was disclosed inthe paper by Sibly, Mcfarland et al. (ethologists) in 1975. Also, Ludlowdisclosed competitive models of behavior in 1976. These results wereargued in the “Old Tricks, New Dogs: Ethology and Interactive Creatures”(April, 1997) by Bruce Mitchell Blumberg (Bruce of Arts, AmherstCollege, 1977; Master of Sciences, Sloan School of Management, MIT,1981). Bruce Mitchell Blumberg applied the above-mentioned theories todogs created by 3D CG (computer graphics) and proved the above theoriesto be a behavior selection mechanism.

It should be reminded that Bruce Mitchell Blumberg verified the behaviorselection mechanism of the animals using CG, not by applying themechanism to any robot apparatuses existing in the substantial space.

For a computer-graphically created virtual creature displayed on adisplay screen of a computer system, it is possible to make a directcoupling between selection and apparition of behavior (behaviorselection=behavior apparition) and so feed back the behavior to itsmotivation by the selection. For an actual robot apparatus, however, theselection and apparition of behavior cannot always be coupled directlyto each other (namely, the behavior selection is not always equal to thebehavior apparition) for the following reasons.

Selected behavior is possibly canceled by behavior effected irrespectiveof schemed behavior such as reflexive one.

Without an input from a sensor, it cannot be known whether behaviorcould have really been done.

An example to which the reason described just above is applicable willbe given below. That is, even when action to “kick the ball with thefoot” is selected when the robot has reached a distance at which it cankick the ball and a behavior command is output (given to the robot), therobot apparatus cannot kick the ball in some cases, for example, if theball lies on a slope. A result that “the ball could successfully bekicked”, of the action to “kick the ball with the foot”, can only berecognized when it has been recognized that the robot apparatus hastouched the ball and the ball has been moved forward. Namely, for thisrecognition, it is necessary to evaluate the behavior based oninformation supplied from a sensor included in the robot apparatus andchange the internal state of the robot apparatus according to the resultof the evaluation.

As seen from the above, the technique proposed by Bruce MitchellBlumberg is not enough to decide the behavior of a robot apparatusexisting in the substantial space.

DISCLOSURE OF THE INVENTION

Accordingly, the present invention has an object to overcome theabove-mentioned drawbacks of the prior art by providing a robotapparatus having an improved likeness to a living thing or a creatureand a method for deciding the behavior of the robot apparatus.

The above object can be attained by providing a robot apparatus whosemoving parts are controlled to make the robot apparatus behaveexpressively, the device including:

means for detecting external or internal information;

means for acquiring a cause factor influencing the behavior from theexternal or internal information detected by the informationdetecting-means;

means for acquiring an occurrence tendency of the causefactor-influenced behavior based on the cause factor acquired by thecause factor-acquiring-means;

means for making a comparison among occurrence tendencies of two or morebehaviors, acquired by the occurrence tendency acquiring means andbelonging to the same group;

means for selecting one of the behaviors based on the result of theoccurrence tendency comparison made by the occurrence tendency comparingmeans; and

means for controlling the moving parts based on the behavior selected bythe behavior selecting means to have the robot apparatus express theselected behavior;

the occurrence tendency of the behavior selected by the behaviorselecting means being varied adaptively to the cause factor which isvariable due to the actual occurrence of the behavior.

In the robot apparatus constructed as above, external or internalinformation is detected by the information detecting-means, a causefactor influencing the behavior is acquired by the cause factoracquiring means from the external or internal information detected bythe information detecting-means, and an occurrence tendency of the causefactor-influenced behavior is acquired by the occurrence tendencyacquiring means based on the cause factor acquired by the cause factoracquiring means.

A comparison is made by the occurrence tendency comparing means amongoccurrence tendencies of two or more behaviors, acquired by theoccurrence tendency acquiring means and belonging to the same group, oneof the behaviors is selected by the behavior selecting means based onthe result of the occurrence tendency comparison made by the occurrencetendency comparing means, and the moving parts are controlled by themoving part controlling means based on the behavior selected by thebehavior selecting means to have the robot apparatus express theselected behavior. The occurrence tendency of the behavior selected bythe behavior selecting means is varied adaptively to the cause factorwhich is variable due to the actual occurrence of the behavior.

The above robot apparatus selects one of the behaviors through acomparison between occurrence tendencies decided under the influence ofthe cause factor, and expresses the behavior as an ethological approach.

Also the above object can be attained by providing a method for decidingthe behavior of a robot apparatus whose moving parts are controlled tohave the robot apparatus behave expressively, the method including thesteps of:

detecting external or internal information of the robot by aninformation detecting-means;

acquiring a cause factor influencing the behavior from the external orinternal information detected in the information detecting step;

acquiring an occurrence tendency of the cause factor-influenced behaviorbased on the cause factor acquired in the cause factor acquiring step;

making a comparison among occurrence tendencies of two or morebehaviors, acquired in the occurrence tendency acquiring step andbelonging to the same group;

selecting one of the behaviors based on the result of the occurrencetendency comparison made in the occurrence tendency comparing step; and

controlling the moving parts based on the behavior selected in thebehavior selecting step to have the robot apparatus express the selectedbehavior;

the occurrence tendency of the behavior selected in the behaviorselecting step being varied adaptively to the cause factor which isvariable due to the actual occurrence of the behavior.

In the above robot apparatus behavior deciding method, external orinternal information is detected in the information detecting step, acause factor influencing the behavior is acquired in the cause factoracquiring step from the external or internal information detected in theinformation detecting step, and an occurrence tendency of the causefactor-influenced behavior is acquired in the occurrence tendencyacquiring step based on the cause factor acquired in the cause factoracquiring step.

A comparison is made in the occurrence tendency comparing step amongoccurrence tendencies of two or more behaviors, acquired in theoccurrence tendency acquiring step and belonging to the same group, oneof the behaviors is selected in the behavior selecting step based on theresult of the occurrence tendency comparison made in the occurrencetendency comparing step, and the moving parts are controlled in themoving part controlling step based on the behavior selected in thebehavior selecting step to have the robot apparatus to express theselected behavior. The occurrence tendency of the behavior selected inthe behavior selecting step is varied adaptively to the cause factorwhich is variable due to the actual occurrence of the behavior.

The above robot apparatus behavior detecting method selects one of thebehaviors through a comparison between occurrence tendencies decidedunder the influence of the cause factor, and expresses the behavior asan ethological approach.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a perspective view of a robot apparatus according to thepresent invention.

FIG. 2 is a block diagram of the circuit configuration of the robotapparatus in FIG. 1.

FIG. 3 is a block diagram of the software configuration of the robotapparatus in FIG. 1.

FIG. 4 is a block diagram of a middleware layer in the softwareconfiguration in the robot apparatus in FIG. 1.

FIG. 5 is a block diagram of the robot apparatus behavior decisionsystem.

FIGS. 6A and 6B explain the robot apparatus whose behavior is decidedusing the ethological approach.

FIG. 7 shows a behavior selection system constructed in the behaviorselector and in which multiple behaviors form a hierarchical structure.

FIG. 8 explains functions of the elements included in the first half ofthe behavior decision system.

FIG. 9 explains functions of the elements included in the second half ofthe behavior decision system.

FIG. 10 is a block diagram explaining the decision of behavior accordingto perception and motivation using the ethological approach.

FIGS. 11A to 11C show characteristic curves of a cause factor statespace in which cause factors are mapped, and an occurrence tendencyspace in which occurrence tendencies of behaviors defined by the causefactor state space are mapped, respectively.

FIG. 12 explains the cause factor state space.

FIGS. 13A and 13B show characteristic curves of ingestive behavior andwater-drinking behavior, respectively, explaining the mathematicaloccurrence of the ethological approach.

FIG. 14 shows a characteristic curve of the occurrence tendency space inwhich there are mapped the ingestive tendency and water-drinkingtendency used for explanation of the mathematical occurrence of theethological approach.

FIGS. 15A and 15B show characteristic curves of a value mapped in thecause factor state space, being in an ingestive behavior selection areaand in a water-drinking behavior selection area, respectively.

FIG. 16 explains the behavior arbitration(exclusive control).

FIG. 17 shows a characteristic curve of the occurrence tendency spaceexplaining the behavior selection implemented by an ethologicalapproach-based mathematical expression.

FIG. 18 shows a characteristic curve of the occurrence tendency spaceexplaining the actual behavior selection by the robot apparatus.

FIG. 19 is a block diagram showing the flow of information among theperceptual information acquisition unit, motivational informationacquisition unit and behavior information selector included in thebehavior selection unit.

FIG. 20 explains the computation of an occurrence tendency before thebehavior arbitration(exclusive control) is done.

FIG. 21 explains the computation of an occurrence tendency under thebehavior arbitration(exclusive control).

FIG. 22 shows a flow of operations made in computation of the occurrencetendency.

FIG. 23 is a block diagram of the perceptual information acquisitionunit.

FIG. 24 shows a flow of operations made in the perceptual informationacquisition unit.

FIG. 25 explains the motivational information acquisition unit.

FIGS. 26A and 26B explain another embodiment of the robot apparatusaccording to the present invention, in which selection of behavior in alower layer of the hierarchy-structure behavior selection system is notinfluenced by any motivation.

FIG. 27 explains functions of the behavior selecting processor andaction generator in the second embodiment of the robot apparatusaccording to the present invention.

FIG. 28 is a block diagram of the behavior decision system in the secondembodiment of the robot apparatus.

FIG. 29 is a block diagram of the action generator.

FIG. 30 is a block diagram of the multiple behavior selection units asobjects.

FIG. 31 shows in detail the first half of each behavior group in thesubsystem layer, mode layer and module layer.

FIG. 32 shows in detail the second half of each behavior group in thesubsystem layer, mode layer and module layer.

BEST MODE FOR CARRYING OUT THE INVENTION

The best mode for carrying out the present invention will be describedin detail with reference to the accompanying drawings. The best modeconcerns a robot apparatus whose instinct and emotion (internal state)are changed based on external and internal factors and which behavesadaptively to the changes of the external and internal factors.

First the construction of the robot apparatus will be described, andthen the applications of the present invention to the robot apparatuswill be described in detail.

(1) Construction of the Robot Apparatus According to the PresentInvention

As shown in FIG. 1, the robot apparatus (referred to simply as “robot”hereafter) is generally indicated with a reference 1. It is a pet robotshaped in the similitude of a “dog”. As shown, the robot 1 includes abody unit 2, leg units 3A to 3D joined to the front right and left andrear right and left, respectively, of the body unit 2, and a head unit 4and tail unit 5 joined to the front and rear ends, respectively, of thebody unit 2.

As shown in FIG. 2, the body unit 2 houses a CPU (central processingunit) 10, DRAM (dynamic random-access memory) 11, flash ROM (read-onlymemory) 12, PC (personal computer) card interface circuit 13 and asignal processing circuit 14, all connected to each other via aninternal bus 15 to form a controller 16, and further a battery 17 tosupply a power to the robot 1. Further the body unit 2 houses an angularvelocity sensor 18 and acceleration sensor 19, to detect the orientationand acceleration of the robot 1, etc.

The head unit 4 houses a CCD (charge coupled device) camera 20 to imagethe environment surrounding the robot 1, a touch sensor 21 to detect apressure given to the robot 1 as physical action such as “patting” or“hitting” by the user, a distance sensor 22 to measure a distance froman object existing before the robot 1, a microphone 23 to collectexternal sounds, a speaker 24 to output a sound such as barking, LEDs(light emitting diode) (not shown) as “eyes” of the robot 1, and so on,located in place, respectively.

Further, actuators 251, 252, . . . and potentiometers 261, 262, . . .are located in joints of the leg units 3A to 3D, articulations betweenthe leg units 3A to 3D and body unit 2, an articulation between the headunit 4 and body unit 2, and in an articulation between a tail 5A andtail unit 5, respectively. The numbers of actuators and potentiometersused in each joint and articulation depend upon the degree of freedom ofthe actuator and potentiometer. For example, each of the actuators 251,252, . . . uses a servo motor. As the servo motor is driven, the legunits 3A to 3D are controlled to shift to a target posture or motion.

Each of the angular velocity sensor 18, acceleration sensor 19, touchsensor 21, distance sensor 22, microphone 23, speaker 24, LEDs,actuators 251, 252, . . . and potentiometers 261, 262, . . . isconnected to the signal processing circuit 14 of the controller 16 via acorresponding one of hubs 271 to 27 n, and CCD camera 20 and battery 17are connected directly to the signal processing circuit 14.

The signal processing circuit 14 sequentially acquires data suppliedfrom each of the above sensors (these will be referred to as “sensordata” hereafter), image data and speech data, and stores each of theminto place in the DRAM 11 via the internal bus 15. Also the signalprocessing circuit 14 sequentially acquires data supplied from thebattery 17 and indicating the remaining potential in the battery 17, andstores each of them into place in the DRAM 11.

Based on each of the sensor data, image data, speech data and remainingbattery potential data thus stored in the DRAM 11, the CPU 10 willcontrol the behavior of the robot 1.

Actually, after the power is initially supplied to the robot 1, the CPU10 reads a control program from a memory card 28 set in a PC card slot(not shown) in the body unit 2 or flash ROM 12 via the PC card interfacecircuit 13 or directly, and stores it into the DRAM 11.

Also, the CPU 10 determines the internal state of the robot 1,environment surrounding the robot 1, the existence of an instruction oraction from the user, etc. based on the sensor data, image data, speechdata, remaining battery potential data sequentially stored from thesignal processing circuit 14 into the DRAM 11 as above.

Further, the CPU 10 decides the next behavior based on the determinationresult and the control program stored in the DRAM 11, and drives thenecessary actuators 251, 252, . . . for the next behavior on the basisof the result of determination to thereby shake or nod the head unit 4,wag the tail 5A of the tail unit 5 or drive the leg units 3A to 3D forwalking.

Also at this time, the CPU 10 generates speech data as necessary, andsupplies it as speech signals to the speaker 24 via the signalprocessing circuit 14, thereby outputting a voice or speech created fromthe speech signals, turning on or off or flickering the LEDs.

Thus, the robot 1 autonomously behaves adaptively to its internal stateor surrounding environment, or an instruction or action from the user.

(2) Software Structure of the Control Program

The above control program for the robot 1 has a software structure asshown in FIG. 3. As shown, a device driver layer 30 is positioned in thelowest layer of the control program, and consists of a device driver set31 including multiple device drivers. In this case, each device driveris an object allowed to make a direct access to the CCD camera 20 (seeFIG. 2) and an ordinary hardware used in a computer such as a timer, andworks with an interruption from an appropriate hardware.

As shown in FIG. 3, a robotic server object 32 is also positioned in thelowest layer of the device driver layer 30. This object 32 consists of,for example, a virtual robot 33 including a software group whichprovides an interface for access to hardware such as the above-mentionedvarious sensors, actuators 251, 252, . . . , etc., a power manager 34including a software group which manages power switching etc., a devicedriver manager 35 including a software group which manages other variousdevice drivers, and a designed robot 36 including a software group whichmanages the mechanism of the robot 1.

There is also provided a manager object 37 consisting of an objectmanager 38 and service manager 39. In this case, the object manager 38is a software group to manage start and termination of each of thesoftware groups included in the robotic server object 32, middlewarelayer 40 and application layer 41, respectively. The service manager 39is a software group which manages the association between objects on thebasis of information on an association between objects stated in anassociation file stored in the memory card 28 (see FIG. 2).

The middleware layer 40 is positioned above the robotic server object 32and consists of a software group which provides basic functions of therobot 1 such as image processing, speech processing, etc. Theapplication layer 41 is positioned above the middleware layer 40 andconsists of a software group which decides behavior of the robot 1 basedon the result of a process effected by each software group included inthe middleware layer 40.

The software structures of the middleware layer 40 and application layer41 are shown in detail in FIG. 4.

As shown in FIG. 4, the middleware layer 40 consists of a recognitionsystem 60 including signal processing modules 50 to 58 intended fornoise detection, temperature detection, brightness detection, scaledetection, distance detection, posture detection, touch sensing, motiondetection and color recognition, respectively, and an input semanticsconverter module 59, and an output system 69 including an outputsemantics converter module 68 and signal processing modules 61 to 67intended for posture management, tracking, motion reproduction, walking,recovery from overturn, LED lighting and speech reproduction,respectively.

The signal processing modules 50 to 58 in the recognition system 60acquire appropriate sensor data, image data and speech data read fromthe DRAM 11 (see FIG. 2) by the virtual robot 33 in the robotic serverobject 32, process the data in a predetermined manner and supply thedata processing result to the input semantics converter module 59. Inthis example, the virtual robot 33 is formed as a function to transferor convert signals under a predetermined communication rule.

Based on the data processing result supplied from the signal processingmodules 50 to 58, the input semantics converter module 59 recognizes theinternal state and surrounding environment of the robot 1 such as“noisy”, “hot”, “bright”, “ball was detected”, “overturn was detected”,“patted”, “hit”, “musical scale was heard”, “moving object was detected”or “obstacle was detected”, and an instruction or action from the user,and outputs the recognition result to the application layer 41 (see FIG.2). Note that the application layer 41 has built therein a behaviordecision system designed for decision of behavior, which will bedescribed in detail later.

On the other hand, in the output system 69, the output semanticsconverter module 68 controls each of the signal processing modules 61 to67 based on behavior information. That is, the output semanticsconverter module 68 responds to the recognition result from therecognition system 60 and outputs to each of the signal processingmodules 61 to 67 control information (commands) adaptively to theinternal state and surrounding environment of the robot 1 such as“noisy”, “hot”, “bright”, “ball was detected”, “overturn was detected”,“patted”, “hit”, “musical scale was heard”, “moving object was detected”or “obstacle was detected”, and an instruction or action from the user.

The behavior information supplied to the output semantics convertermodule 68 includes abstract behavior command such as “go ahead”, “joy”,“whine” or “track(a all)”. The output semantics converter module 68supplies such behavior commands to each of the signal processing modules61 to 67. The behavior information supplied to the output semanticsconverter module 68 originates from the behavior decision system whichis a higher-order information processing system. The behavior decisionsystem forms an essential part of the present invention, and will bedescribed in detail later.

Each of the signal processing modules 61 to 67 works based on thebehavior command from the output semantics converter module 68 to outputa control signal for controlling each device to the virtual robot 33.More particularly, the signal processing modules 61 to 67 generate aservo command, sound data and/or drive data based on the receivedbehavior command, and sequentially sends them to the actuators 251, 252,. . . (in FIG. 2), speaker 24 (in FIG. 2) and/or “eye's” LEDs,respectively, via the virtual robot 33 in the robotic server object 32(in FIG. 3) and signal processing circuit 14 (in FIG. 2) in this order.

With each device controlled based on the signal (command) from thevirtual robot 33, the robot 1 behaves in a predetermined manner.

Next, the behavior decision system is described that decides the nextbehavior (transitional action or intended action) based on a recognitionresult from the input semantics converter module 59 and outputinformation on the thus decided behavior to the output semanticsconverter module 68.

(3) Configuration of the Robot Behavior Decision System

The robot 1 decides a behavior (will also be referred to as “behavior”hereafter wherever appropriate) by a behavior decision system 70 asshown in FIG. 5. The behavior decision system 70 decides a behaviorbased on the recognition result from the input semantics convertermodule 59, and outputs information on the behavior to the outputsemantics converter module 68. As shown in FIG. 5, the behavior decisionsystem 70 includes a behavior selection unit 80, internal-state modelunit 71 and a modulator 72.

The behavior selection unit 80 selects the desired one from a set ofbehaviors. More specifically, the behavior selection unit 80 selects thedesired behavior based on the recognition result from the inputsemantics converter module 59. The behavior selection unit 80 includesfor example a perceptual information acquisition unit 90, motivationalinformation acquisition unit 81 and a behavior selecting processor 82 toselect a behavior.

In the behavior decision system 70, the perceptual informationacquisition unit 90 and motivational information acquisition unit 81function to acquire a cause factor being external or internalinformation detected by a detecting-means for detecting external orinternal information, such as the CCD camera 20, distance sensor 22,microphone 23 or the like, and which influences the robot behavior, andto acquire an occurrence tendency of the behavior influenced by a causefactor detected by the cause factor acquiring means. The behaviorselecting processor 82 in the behavior decision system 70 works to makea comparison among occurrence tendencies of two or more behaviors,acquired by the perceptual information acquisition unit 90 andmotivational information acquisition unit 81 and belonging to the samegroup, and to select one of the behaviors based on the result of theoccurrence tendency comparison thus made. Also in the behavior decisionsystem 70, the output semantics converter module 68 controls the movingparts based on the behavior selected by the behavior selecting processor82 to have the robot 1 express the selected behavior.

Then, the behavior selection unit 80 selects a behavior by means of thebehavior selecting processor 82 based on the perceptual informationacquired by the perceptual information acquisition unit 90 from therecognition result and motivational information acquired by themotivational information acquisition unit 81 from the internal stateinformation supplied from the internal-state model unit 71. The behaviorselection unit 80 will be described in detail later.

On the other hand, the internal-state model unit 71 has aninternal-state model which changes the instinct and emotion (internalstate) of the robot 1 adaptively to the external and internal factors.The term “external factor” used herein refers to for example “hit”information, “patted” information, an instruction from the user of therobot 1 or the like. The term “internal factor” refers to for exampleinformation of “battery potential has become lower”, information of“body temperature has risen” or the like.

More specifically, the internal-state model unit 71 changes the internalstate based on the recognition result supplied from the input semanticsconverter module 59 and outputs the internal state information to thebehavior selection unit 80 and modulator 72.

The motivational information acquisition unit 81 acquires motivationalinformation based on the above internal state information, which will bedescribed in detail later.

On the other hand, the modulator 72 generates behavior information(behavior command) on a behavior to finally be expressed by the robot 1.More specifically, the modulator 72 generates behavior information tofinally be expressed from a behavior selected by the behavior selectionunit 80 and internal state information supplied from the internal-statemodel unit 71 and outputs the data to the output semantics convertermodule 68.

The modulator 72 can have the robot 1 express a behavior combined withthe behavior decided (selected) by the behavior selection unit 80 andinstinct and emotional states supplied from the internal-state modelunit 71, combined with the behavior. That is, the behavior selectionunit 80 selects a behavior “eat an apple” as the next behavior based onthe recognition result and the like, while the internal-state model unit71 acquires for example a state “angry” as the current internal state ofthe robot 1 based on the recognition result. Then, the modulator 72combines the internal state “angry” with the behavior “eat an apple”based on the information and thus generates behavior information of “eatan apple angrily”, and outputs the information to the output semanticsconverter module 68. The output semantics converter module 68 willsignal each of the signal processing modules 61 to 67 to control eachdevice which will in turn control each moving part, whereby the robot 1is caused to express the next behavior (intended behavior), namely, toeat the apple angrily.

Also the internal state information indicative of the instinct andemotional states generated in the internal-state model unit 71 is usedwhen deciding (selecting) a behavior of the robot 1, and also ascombined with the decided behavior.

As above, the behavior decision system 70 decides a behavior based onthe result of recognition. Each component of the behavior decisionsystem 70 will be described in further detail below.

(3-1) Construction of the Internal-state Model Unit

The internal-state model unit 71 changes the internal state such as theinstinct and emotion adaptively to external and internal factors. Theinstinct and emotional states supplied from the internal-state modelunit 71 are used when deciding a behavior of the robot 1, and also ascombined with the decided behavior.

The internal-state model unit 71 consists of a set of elements relatedto instinct (desire) and character which vary adaptively to external andinternal factors.

More specifically, the internal-state model unit 71 includes a total of27 elements indicative of internal state, whose 9 instinctive elementsare “fatigue”, “temperature”, “pain”, “hunger”, “thirst”, “affection”,“curiosity”, “elimination” and “sexual”, and 18 emotional elements are“happiness”, “sadness”, “anger”, “surprise”, “disgust”, “fear”,“frustration”, “boredom”, “somnolence”, “gregariousness”, “patience”,“tense”, “relaxed”, “alertness”, “guilt”, “spite”, “loyalty”,“submission” and “jealousy”.

Each of the above emotional elements holds a parameter indicative of theintensity thereof. The internal-state model unit 71 cyclically changesthe parameter of each of these elements based on a specific recognitionresult such as “hit” or “patted” supplied from the input semanticsconverter module 59, elapsed time, etc.

More particularly, the emotional elements uses a predetermined algorithmto compute a variation of the emotion at a time from a recognitionresult supplied from the input semantics converter module 59, behaviorof the robot 1 at that time and elapsed time from the last renewal.Then, taking the emotion variation as ΔE[t], current parametric value ofthe emotion as E[t] and coefficient indicating the sensitivity to theemotion as k_(e), the internal-state model unit 71 determines aparametric value E[t+1] of the emotion in the next cycle by computing anequation (1), and replaces the emotion parametric value E[t+1] with thecurrent parametric value E[t] of the emotion, to replace the previousparametric value of the emotion.

E[t+1]=E[t]+k _(e) ×ΔE[t]  (1)

The internal-state model unit 71 similarly computes the equation (1) torenew the parametric values of all the remaining emotions such as“happiness”.

Note that it is predetermined how much the recognition result andinformation from the output semantics converter module 68 influences thevariation ΔE[t] of the parametric value of each emotion. Thepredetermination is such that for example, the result of recognition of“hit” will have a great influence on the variation ΔE[t] of theparametric value of the “anger” emotion, while the result of recognitionof “patted” will have a great influence on the variation ΔE[t] of theparametric value of the “joy” emotion.

The information from the output semantics converter module 68 is feedback information on behavior (behavior-completion information). Namely,it is information on the result of behavior expression. Theinternal-state model unit 71 will change the emotion with suchinformation, and also the instinct as will be described in detail later.

For example, “whining” behavior will lower the level of “anger” emotion.Note that the result of behavior may be fed back by an output (behaviorhaving a feeling added thereto) of the modulator 72.

On the other hand, each desire (instinct) holds a parameter indicativeof the extent thereof The internal-state model unit 71 cyclically renewsthe parametric value of each instinctive element included in theinstinctive elements on the basis of a recognition result supplied fromthe input semantics converter module 59, elapsed time and informationfrom the output semantics converter 68.

More particularly, the internal-state model unit 71 uses a predeterminedalgorithm to compute a variation of each instinct (desire) “fatigue”,“affection”, “curiosity”, “sexual” and “elimination” at a time from arecognition result, elapsed time and information from the outputsemantics converter module 68. Then, taking the desire variation asΔI[k], current parametric value of the desire as I[k] and coefficientindicating the sensitivity to the desire as K_(i), the internal-statemodel unit 71 determines a parametric value I[k+1] of the desire in thenext cycle by computing an equation (2) in a given cycle, and replacesthe value I[k+1] with the current parametric value I[k] of the desire,to replace the previous parametric value of the desire.

I[k+1]=I[k]+k _(i) ×ΔI[k]  (2)

The internal-state model unit 71 also computes the above equation (2) torenew the parametric values of all the remaining elements of instinct(desire) such as “fatigue” in the same manner.

Note that it is predetermined how much the recognition result andinformation from the output semantics converter module 68 influences thevariation ΔI[k] of the parametric value of each desire. Thepredetermination is such that for example, information from the outputsemantics converter module 68 will have a great influence on thevariation ΔI[k] of the parametric value of “fatigue” state.

Also, the parametric value of a predetermined desire can be determinedas described below.

For the “pain” element included in the instinctive elements, the numberof times abnormal posture has been taken is taken as N, extent of thepain is taken as K1 and velocity of pain alleviation is taken as K₂based on the number of times the abnormal posture received from theposture detecting signal processing module 55 in the middleware layer 40via h input semantics converter module 59, and a parametric value I[k]of the “pain” is computed using the following equation (3), and theresult of the computation is replaced with the parametric value I[k] ofthe current pain, thereby changing the parametric value of the “pain”.When I[k]<0, I[k]=0, t=0 and N=0.

I[k]=K ₁ ×N−K ₂ ×t  (3)

For the instinctive element “temperature”, the temperature is taken asT, outside air temperature is as Y₀ and coefficient of temperatureelevation is as K3 based on temperature data supplied from thetemperature detecting signal processing module 51 via the inputsemantics converter module 59. A parametric value I[k] of the“temperature” is computed using the following equation (4), and theresult of the computation is replaced with the parametric value I[k] ofthe current temperature, thereby renewing the parametric value of the“temperature”. When T−T₀<0, I[k]=0.

I[k]=(T−T ₀)×K ₃  (4)

For the instinctive element “hunger”, the remaining battery potential istaken as BL based on remaining battery potential data (informationacquired by a remaining batter potential detecting module (not shown))supplied via the input semantics converter module 59, a parametric valueI[k] of the “hunger” is computed using the following equation (5) in apredetermined cycle, and the result of the computation is replaced withthe parametric value I[k] of the current hunger, thereby renewing theparametric value of the “hunger”.

I[k]=100−B _(L)  (5)

For the instinctive element “thirst”, it is assumed that based on thechanging speed of the remaining battery potential data supplied via theinput semantics converter module 59, the remaining battery potential istaken as B_(L)(t) at a time t and remaining battery potentials areacquired at times t_(1 and t) ₂, respectively. Then a parametric valueI[k] of the “thirst” is computed using the following equation (6), andthe result of the computation is replaced with the parametric value I[k]of the current thirst, thereby renewing the parametric value of the“thirst”.

I[k]={B _(L)(t ₂)−B _(L)(t ₁)}/(t ₂ −t ₁)  (6)

Note that in this embodiment, the parametric values of each the emotionsand desire elements (instinct) are defined to vary within a range of 0to 100, and the coefficients k_(e) and k_(i) are also set for each ofthe emotions and desire elements.

The internal-state model unit 71 is constructed as above, and the robot1 is adapted to autonomously behave with the instinct (desired) andemotional states (parameters) changed by the internal-state model unit71 adaptively to its own internal state and the environmental conditionin which the robot 1 exists.

(3-2) Instinct and Emotion Changes Corresponding to the Environment

In addition, the robot 1 adapts the emotions and instincts to the valuesof three of the ambient conditions, “noise”, “temperature” and“illumination” (these will be referred to as “environmental conditions”hereafter). That is, for example, when the environment is “bright”, therobot 1 becomes bright or cheerful, but when the robot 1 is in the“dark”, it will be calm.

More specifically, the robot 1 includes, in addition to the previouslymentioned CCD camera 20, distance sensor 22, touch sensor 21, microphone23, etc., a temperature sensor (not shown) provided in place to detectthe ambient temperature and which works as one of the external sensorsto detect environmental conditions. According to the temperature sensor,the recognition system 60 in the middleware layer 40 includes signalprocessing modules 50 to 52 to detect the noise, temperature andbrightness, respectively.

The noise-detecting signal processing module 50 is adapted to detect thelevel of ambient noise based on speech data provided by the microphone23 (see FIG. 2) via the virtual robot 33 in the robotic server object32, and outputs the detection result to the input semantics convertermodule 59.

Also, the temperature-detecting signal processing module 51 is adaptedto detect an ambient temperature based on sensor data supplied from thethermosensor via the virtual robot 33, and outputs the detection resultto the input semantics converter module 59.

Further, the brightness-detecting signal processing module 52 is adaptedto detect an ambient illumination based on image data supplied from theCCD camera 20 (see FIG. 2) via the virtual robot 33, and outputs thedetection result to the input semantics converter module 59.

The input semantics converter module 59 recognizes the level of each ofthe ambient “noise”, “temperature” and “illumination” based on theoutputs from the signal processing modules 50 to 52, and outputs therecognition result to the internal-state model unit 71 of theapplication module 41 (see FIG. 5).

More specifically, the input semantics converter module 59 recognizesthe level of ambient “noise” based on an output from the noise detectingsignal processing module 50, and outputs a recognition result like“noisy” or “quiet” to the internal-state model unit 71.

Also the input semantics converter module 59 recognizes the level ofambient “temperature” based on an output from the temperature detectingsignal processing module 51, and outputs a recognition result like “hot”or “cold” to the internal-state model unit 71 and perceptual informationacquisition unit 90.

Further the input semantics converter module 59 recognizes the intensityof ambient “illumination” based on an output from the brightnessdetecting signal processing module 52, and outputs a recognition resultlike “bright” or “dark” to the internal-state model unit 71.

The internal-state model unit 71 cyclically changes the parametricvalues by computing the equation (1) based on the various recognitionresults supplied from the input semantics converter module 59 as above.

Then the internal-state model unit 71 increases or decreases the valueof the coefficient k_(e) in equation (1) for a predetermined appropriateemotion based on the recognition results regarding “noise”,“temperature” and “illumination” supplied from the input semanticsconverter module 59.

More particularly, for example, when a recognition result “noisy” issupplied, the internal-state model unit 71 will increase the value ofthe coefficient k_(e) for the “anger” emotion by a predetermined number.On the other hand, when the recognition result supplied is “quiet”, theinternal-state model unit 71 will decrease the value of the coefficientk_(e) for the “anger” emotion by a predetermined number. Thereby, theparametric value of the “anger” emotion will be changed under theinfluence of the ambient “noise”.

Also, when a recognition result “hot” is supplied, the internal-statemodel unit 71 will decrease the value of the coefficient k_(e) for the“joy” emotion by a predetermined number. On the other hand, when therecognition result supplied is “cold”, the internal-state model unit 71will increase the value of the coefficient k_(e) for the “sadness”emotion by a predetermined number. Thus, the parametric value of the“sadness” emotion will be changed under the influence of the ambient“temperature”.

Further, when a recognition result “bright” is supplied, theinternal-state model unit 71 will decrease the value of the coefficientk_(e) for the “joy” emotion by a predetermined number. On the otherhand, when the recognition result supplied is “dark”, the internal-statemodel unit 71 will increase the value of the coefficient k_(e) for the“fear” emotion by a predetermined number. Thus, the parametric value ofthe “fear” emotion will be changed under the influence of the ambient“illumination”.

Similarly, the internal-state model unit 71 cyclically changes theparametric value of each of the desire elements by computing theequations (2) to (6) based on various recognition results supplied fromthe input semantics converter module 59 as above.

Also, the internal-state model unit 71 increases or decreases the valueof the coefficient k_(i) in equation (2) for a predetermined appropriatedesire based on the recognition results regarding “noise”, “temperature”and “illumination” supplied from the input semantics converter module59.

Also, for example, when recognition results “noisy” and “bright” aresupplied, the internal-state model unit 71 will decrease the value ofthe coefficient k_(i) for the “fatigue” state by a predetermined number.On the other hand, when the recognition results supplied are “quiet” and“dark”, the internal-state model unit 71 will increase the value of thecoefficient k_(i) for the “fatigue” state by a predetermined number.Further, for example, when a recognition result “hot” or “cold”, theinternal-state model unit 71 will increase the value of the coefficientk_(i) for the “fatigue” by a predetermined number.

Thus, as a result, when the robot 1 is in a “noisy” environment forexample, the parametric value of the “anger” emotion readily increaseswhile that of the “fatigue” state readily decreases, so that the robot 1will express “irritated behavior. On the other hand, when theenvironment surrounding the robot 1 is “quiet”, the parametric value ofthe “anger” emotion readily decreases while that of the “fatigue” statereadily increases, so that the robot 1 will act “gently.

Also, when the robot 1 is in a “hot” environment, the parametric valueof the “joy” emotion readily decreases while that of the “fatigue” statereadily increases, so the robot 1 will exhibit “lazy” behavior. On theother hand, when the robot 1 is in a “cold” environment, the parametricvalue of the “sadness” emotion readily increases while that of the“fatigue” state readily increases, so the robot 1 will act as ifaffected by the cold.

When the robot 1 is in the “bright” environment, the parametric value ofthe “joy” emotion readily increases while that of the “fatigue” statereadily decreases, so that the robot 1 will exhibit “cheerful” behavior.On the other hand, in the “dark” environment, the parametric value ofthe “joy” emotion readily increases while that of the “fatigue” statereadily increases, so that the robot 1 will behave “evenly”.

Thus the robot 1 can change its instinct and emotional states adaptivelyto the environment (external and internal factors) by means of theinternal-state model unit 71 and expresses the changed instinct andemotional states by its behavior. Further, the instinct and emotionalstates acquired by the internal-state model unit 71 are used asinformation for selection of behavior in the behavior selection unit 80.

(3-3) Construction of the Behavior Selection Unit

The behavior selection unit 80 selects one of a set of behaviorsprepared in advance. The behavior selection unit 80 is constructed toselect (decide) behavior using the ethological approach.

Generally, an animal is considered to decide a behavior based onmultiple external and internal factors (generically referred to as“cause factor” hereafter) which influence the animal's behavior. Thecause factors are complicatedly intertwined with each other. The robot 1is designed based on general behavior decision mechanism of the animalto decide a behavior.

The robot 1 having a behavior decision mechanism constructed using theethological approach will decide a behavior to express by following theprocedure below for example when there is a pool in front of the robot 1as shown in FIG. 6A.

The robot 1 will “find water” and perceive and recognize (evaluate) anexternal cause factor (based on an external perceptual element; forexample, perception) “10 cm to water”. On the other hand, the robot 1has motivations “high thirst” and “medium level of anger” as theinternal cause factor (based on internal motivational element; forexample, instinct and emotion). Note that the motivation is acquiredusing a parametric value from the aforementioned internal-state modelunit 71, which will be described in detail later.

In the behavior decision based on the ethological approach, there ismade at least the following judgment.

Namely, even when the robot 1 is in a “highly thirsty” state and has“found water”, it will not always express any water-drinking behavior ifthe distance from water is long. For example when water is distant fromthe robot 1, the latter will possibly be in a degraded conditioncorrespondingly and have a higher thirst. In this case, the robot 1 willinstinctively avoid the water-drinking behavior.

On the contrary, even when the robot 1 has a “lower thirst” and “thereis water in front thereof”, it will express the water-drinking behaviorin some cases. Namely, it is not always judged based on the internalcause factor “thirst” whether the robot 1 expresses the water-drinkingbehavior, but judgment for the behavior decision is made based on theexternal cause factors “there is water” and “it exists in front” of therobot 1. That is, a behavior is decided (selected) based on multipleexternal and internal cause factors complicatedly intertwined with eachother.

The behavior is compared to others before finally deciding a behavior.For example when the robot 1 wants to “drink water” and “eat”, it makesa comparison between the extent or feasibility of the desire to “drinkwater” is compared with the extent or feasibility of the desire to “eat”and selects for example the water-drinking behavior as one of thepossible behaviors.

Based on the ethological approach, the robot 1 finally decides abehavior. That is, with the internal state such as “high thirst”, therobot 1 makes an overall judgment based on the information “finding ofwater” and “distance of 10 cm to water” to express the water-drinkingbehavior while excluding any other behaviors such as “eating” behavior.

Also, the robot 1 expresses the “eating” behavior with anger as thestate “at medium level of anger”. The behavior expression is provided bythe aforementioned modulator 72. Then, in the robot 1, the anger as theinternal state is lowered in level due to the “finding of water”. Theanger level is lowered by feed back behavior completion information fromthe output semantics converter module 68 to the internal-state modelunit 71.

FIG. 6B shows the procedure for selecting actions down to “walk forward”as the “water-drinking behavior” based on the aforementioned ethologicalapproach.

First, when in the state as shown in FIG. 6A, the robot 1 selects“ingestive behavior” from among multiple behaviors including “ingestivebehavior”, “agonistic behavior”, “investigative behavior”, etc. Therobot 1 holds a subsystem (subsystem layer) as a group of selectablebehaviors including the “ingestive behavior”, “agonistic behavior”,“investigative behavior”, etc.

The behavior group includes multiple low-order behavior groups whichtogether form a high-order behavior. The low-order behavior groupscontrol each other, which is also true for the following.

Next, the robot 1 selects “water-drinking behavior” from the selectedingestive-behaviors. The ingestive-behaviors include also “eating”behavior. For example, the robot 1 holds modes (mode layer) in which agroup of selectable behaviors such as “water drinking” behavior,“eating” behavior are included. That is, the robot 1 holds the behaviorgroup including “water drinking” and “eating” behaviors as behaviorssubordinate to the “ingestive” behavior subsystem.

Next, the robot 1 selects to “move forward” for “go-to-water” behaviorand expresses the behavior. For the “go-to-water” behavior, possiblebehaviors include “move backward”, “turn to right”, “turn to left”. Therobot 1 holds motor commands (command layer) including the “moveforward”, “move backward”, “turn to right”, “turn to left”, etc.

By following the above procedure, the robot 1 takes the ethologicalapproach to express the bottom-layer behavior like “walk forward” as thefinal behavior of the high-order behaviors included in the “ingestivebehavior” subsystem.

FIG. 7 shows a behavior selection system built for decision selection.The behavior selection system is formed in the behavior selection unit80.

In the behavior selection system, a set of behaviors is organized in theform of the hierarchical structure (tree structure). In this system, thehigher layer includes abstract behaviors such as desire elements. In thehierarchical-structure behavior selection system includes a behaviorgroup consisting of a set of low-order behaviors which together form ahigh-order behavior. For example, when the robot 1 exhibits a high-orderbehavior like “agonistic behavior”, lower-order behaviors include“fighting/predation”, “defense/eacape”, etc.

The behavior selection system may be designed to hold each behavior inthe form of data (e.g., in the form of a data base), namely, it may bedesigned for an object-oriented system for example. When the behaviorselection unit is designed as an object-oriented type, the behaviorselection system is constructed to have behaviors as independent unitsof an object and operates with each unit of the object for selection ofa behavior.

In the behavior selection system in which the set of behaviors isorganized in the form of the hierarchical structure as shown in FIG. 7,the behaviors in the high-order layer are abstract ones such as desirewhile those in the low-order layer are concrete ones to realize thedesire.

In such a behavior selection system, selection is made through behaviorsin the low-order layer, that is, there is selected a behavior to realizea high-order behavior, namely, a final behavior. That is, behaviors inthe middle layer contain information on a path extending from thehighest-order behaviors to the lowest-order ones.

While proceeding along the above path from the high-order layer to thelow-order layer, a behavior is selected in each of the layers based onthe aforementioned external and internal cause factors.

As shown in FIG. 5, the behavior selection unit 80 includes theperceptual information acquisition unit 90, motivational informationacquisition unit 81 and behavior selecting processor 82. Each of theseelements of the behavior selection unit 80 will function as outlinedbelow with reference to FIGS. 8 and 9.

The perceptual information acquisition unit 90 acquires perceptualinformation for each of behaviors. For acquisition of perceptualinformation, the perceptual information acquisition unit 90 computes anRM (release mechanism) value indicative of an evaluation of theperception in a release mechanism which will be described in detaillater. When the perceptual information acquisition unit 90 finds “water”and recognizes that the robot 1 is at a distance of 10 cm from the“water”, the value of the ingestive behavior (water-drinking behavior)will be larger, that is, the water-drinking behavior will likely beselected.

The motivational information acquisition unit 81 acquires motivationalinformation for each behavior based on the internal state of the robot1. For acquisition of motivational information for each behavior, forexample, It computes a motivation for each behavior based on theaforementioned instinct and emotion values. More specifically, itcomputes a Mot value indicative of the state of a motivation in amotivation creator which will be described in detail later. Themotivational information acquisition unit 81 acquires the thirsty stateof the robot 1. Thus, the motivation value of the ingestive behaviorwill be larger and the water-drinking behavior included in the ingestivebehavior will have a further value.

The behavior selecting processor 82 selects a desired behavior based onmotivational information (motivation value) from the motivationalinformation acquisition unit 81 and perceptual information (value) fromthe perceptual information acquisition unit 90. When selecting thedesired behavior, the behavior selecting processor 82 arbitrates otherelements of behavior belonging to the same group of behaviors. Forexample, the behavior selecting processor 82 selects the ingestivebehavior in the subsystem layer and selects the water-drinking behaviorin the ingestive behavior.

Also, the behavior selecting processor 82 programs actual motion groupsbased on the selected behavior. By way of example, such a programedmotion group is to select “move-forward”.

Note that the internal-state model unit 71 acquires information oninternal state such as instinct and emotional states of the robot 1 asabove. For example, for acquisition of internal-state information, theinternal-state model unit 71 computes instinct and emotion values. Morespecifically, the internal-state model unit 71 computes parametricvalues of the instinct (desire) and emotion or an IE value which will bedescribed in detail later. For example, the internal-state model unit 71acquires information on the thirsty state caused by motion or the like.

As shown in FIG. 8, the output semantics converter module 68 converts abehavior to a sequence of motions corresponding to the type of robot 1.For example, when the output semantics converter module 68 recognizesthat the robot 1 is of a quadruped type, it will provide a sequence ofmotions corresponding to an input behavior and the emotion state of therobot 1. Namely, the output semantics converter module 68 sends acommand to the signal processing modules 61 to 67 based on a behaviorcommand from the higher-order behavior decision system 70.

The modulator 72, posture management module, etc. shown in FIG. 9 willbe described in detail later. Note that in FIG. 9, the “input” columnshows shapes of input commands while the “output” column shows shapes ofoutput commands.

The behavior selection unit 80 is constructed as above. Next, theethological approach adopted for the behavior selection by the behaviorselection unit 80 will be described below.

(3-4) Behavior Selection Using the Ethological Approach

Generally, behavior of an animal is decided (selected) based on a set offactors complicatedly intertwined with each other. FIG. 10 shows asimple example in which a behavior is decided based on perceptual andmotivational information.

The perception is external information which influences the behavior andmay be considered as a condition motivated or restricted by inputenvironmental information. The motivation is internal information suchas “hungry” or the like expressing an internal state and may beconsidered as an internal intention to express the behavior. Thus,perception and motivation can be used as a cause to decide a behavior toenact.

A behavior is decided based on perception and motivation as described indetail below. Note that the following principle of behavior decision(selection) is based on the state space approach having been proposed bySilby and Mcfarland (1975).

The theory of Silby and Mcfarland (1975) is based on the assumption thatthe animal most likely takes an action (behavior) it has expressed morefrequently. An occurrence tendency can be clearly defined by a vectorspace. The magnitude of a vector indicates a so-called occurrencetendency magnitude based on an index having a certain commonality. Theoccurrence tendency includes for example a tendency (degree) with whichan ingestive behavior occurs and a tendency (degree) with which awater-drinking behavior occurs. All the occurrence tendencies aredepicted as points in the occurrence tendency space.

The occurrence tendency space is divided into areas each showing similaroccurrences of behavior and separated by a switching line.

On the other hand, the occurrence tendency depends upon various causefactors. For example, the eating behavior tendency depends upon thelimits of food, opportunity of ingestion, possibility of predation, etc.Another vector space is used to clearly indicate all these causefactors. Decision of an occurrence tendency based on cause factors isbased on the following. A mapping is made from a state space of causefactors to an occurrence tendency space to provide a state space ofoccurrence tendencies, adaptive to any states of cause factors. Abehavior can be decided in the occurrence tendency state space. Therelations between the cause factors and occurrence tendencies will bedescribed below with reference to FIGS. 11A to 11C.

FIGS. 11A and 11C show the cause factor state space depicting the statesof cause factors. The cause factor state space consists of cause factorswhich influence the conduct of a behavior. The cause factors include theaforementioned “perception” and “motivation”. Note that FIGS. 11A to 11Cshow only the two-dimensional space for the simplicity of illustrationand explanation but actually, many of behavior occurrence tendencies aredecided based on a cause factor state space of three or more dimensions.

FIG. 11A shows a tendency for eating-behavior, namely, a tendency ofeating-behavior occurrences (referred to as “eating tendency”hereafter). In FIG. 11A, the horizontal axis indicates a motivation“hunger” as being one of the cause factors while the vertical axisindicates the perception “deliciousness” as being another cause factor.FIG. 11C shows a tendency of water-drinking behavior, namely, a tendencyof “water drinking” behavior occurrences (referred to as “water-drinkingtendency” hereafter). In FIG. 11C, the horizontal axis indicates“thirst” as “motivation” while the vertical axis indicates “distancefrom water” as “perception”.

FIG. 11B shows a space of “eating tendency” and “water-drinkingtendency” based on the cause factors in FIGS. 11A and 11C. Namely, FIG.11B shows the space in which an occurrence tendency of a behaviorinfluenced by the cause factors is mapped for comparison between theeating and water-drinking tendencies.

First, the cause factor state space is described with reference to FIG.12. The cause factor state space in FIG. 12 is that of “eating behavior”shown in FIG. 11A.

As seen from FIG. 12, when there is very delicious food (m₂ state) andthe hunger is not very strong (cause state) (n1 state) or when thehunger is very strong (n₂>n₁) and the available food is not so delicious(cause state) (_(M1)<m₂), eating behavior occurs. That is, the eatingbehavior cannot always be said to occur depending solely upon themotivation “hunger” nor depends solely upon the perception“deliciousness”, but it will occur depending on the interaction between“hunger” and “deliciousness”.

In other words, even with different degrees of “hunger”, eating behavioroccurs. At a set of points in the cause factor state space, there existcause states “hunger” and “deliciousness” which cause occurrences ofeating-behavior to be equivalent to each other, namely, resulting insimilar degrees of eating tendencies. For example, the eating tendencywhen a very “delicious” food is given when there is no hunger isgenerally the same as that when a food “not so delicious” is given whenthere is a strong hunger.

For example, it assumed here that “hunger” and “deliciousness” are takenas cause factors in eating behavior. For the degrees of the eatingbehavior occurrence tendencies to be similar to each other, the “hunger”is low while the “deliciousness” is high or the “hunger” is high whilethe “deliciousness” is low. Therefore, the “hunger” and “deliciousness”are inversely proportional to each other for similar degrees of eatingbehavior occurrence tendencies. Connecting points of similar eatingtendencies to each other results in a curve, for example, as shown inFIG. 12. As seen, there exist a set of cause factor states in which theeating tendencies are similar in strength (vector magnitude) y to eachother and the cause factor states are depicted as a curve in the causefactor state space.

In the cause factor state space, there exist a set of eating tendenciesdifferent in strength (y₁, y₂, . . . ) to each other, depicting contoursof the eating tendency strength as shown in FIG. 12.

In FIG. 12, the eating tendency is stronger as it goes upward in thecause factor state space, which means that everyone will show eatingbehavior when he or she is very hungry and there is very delicious foodbefore him.

Thus, the strength of eating tendency can be defined with cause factorsand the strength of water-drinking tendency can be defined similarly.

That is to say, when there is a strong thirst, the water-drinkingbehavior will occur even if the distance from water is long. Also, whenthe thirst is weak but the distance from water is short, thewater-drinking behavior will occur as the result of the interactionbetween the “thirst” and “distance from water”.

In other words, the water-drinking behavior occurs irrespective of thedegree of the “thirst”, strong or weak. At a set of points in the causefactor state space, there are cause states of similar water-drinkingtendencies based on the “thirst” and “distance from water”. For example,the water-drinking tendency when there is no “thirst” but there is waterin a very near place is similar to that when the “thirst” is very strongbut water is in a very far place.

For similar degrees of water-drinking tendencies, the “thirst” and“distance from water” are inversely proportional to each other.Connecting points of similar degrees of water-drinking tendencies willresult in a curve in the cause factor state space as shown in FIG. 11C.Namely, there are a set of cause states similar in strength x ofwater-drinking tendency to each other and they are depicted as a curvein the cause factor state space as in FIG. 11C in which there are shownwater-drinking tendencies different in strength (x₁, x₂, . . . ) ascontours.

As above, the strength of “eating tendency” and that of “water-drinkingtendency” are determined based on the cause factor states, thetendencies are compared with each other based on their strength and oneof the behaviors is decided (selected). The occurrence tendencies arecompared with each other in the occurrence tendency space as shown inFIG. 11B. The occurrence tendency space consists of tendencies ofbehaviors which can occur.

For example, when the strength y₁ of an eating tendency and strength x₂of a water-drinking tendency are detected in a cause state, the strengthy₁ of eating tendency and strength x₂ of water-drinking tendency, mappedfrom the cause factor state space, are combined with each other in theoccurrence tendency space as shown in FIG. 11B, for the purpose ofcomparison. More specifically, a behavior is selected as describedbelow.

As shown in FIG. 11B, the occurrence tendency space is divided into twoareas by a switching line. One of the areas is defined by the switchingline and the x-axis indicating (y=0) the water-drinking tendency (thisarea will be referred to as water-drinking behavior selecting area), andthe other is defined by the switching line and the y-axis indicating theeating tendency (x=0) (this area will be referred to as Aeating behaviorselecting area).

In each of the areas defined by the switching line in the occurrencetendency space, one behavior is decided based on the position of a value(x, y) mapped from the cause factor state space. That is, when the value(x, y) is found in the water-drinking behavior selecting area, thewater-drinking behavior will be selected, and when the value (x, y) liesin the eating behavior selecting area, the eating behavior will beselected. Therefore, in the example shown in FIG. 11C, since the valuex₂, y₁) lies in water-drinking behavior selecting area, thewater-drinking behavior will be selected.

Note that the cause factor state space is shown for the state variable(cause factor) for each of the eating and water-drinking behaviors forthe simplicity of illustration and explanation. Actually, however, onestate variable will influence the occurrence tendencies of a set ofbehaviors. The curves in the cause factor space are connected to a statein which the level of the occurrence tendency of a specific behavior isattained.

Also, a behavior finally selected will possibly influence the causefactors for the behavior as well as a set of other cause factors. Forthis reason, information is arbitrated.

The behavior decision (selection) method using cause factors for theethological approach was proposed by Silby and Mcfarland (in 1975) andLudlow (competitive model) for example.

(3-5) Formulae for Enabling Behavior Decision Using the EthologicalApproach

The ethological approach for the above-mentioned behavior decision isjust theoretical, and for application thereof to the actual robot 1, theaforementioned ethological approach must be computerized or encoded asdata base. To implement the present invention, the ethologicalapproach-based behavior decision is encoded as follows:

As shown in FIG. 13A, the “hungry” state (degree) as the cause factor of“eating behavior” is taken as Mot[0], and “deliciousness” is evaluatedas RM[0]. The eating tendency (tendency strength) when Mot[0] and RM[0]take certain values, respectively, is taken as Be[0].

Similarly, as shown in FIG. 13B, the “thirsty” state (degree) as thecause factor of “water-drinking behavior” is taken as Mot[1] and“distance from water” is evaluated as RM[1]. The water-drinking tendency(tendency strength) when Mot[1] and RM[1] have certain values,respectively, is taken as Be[1]. These items are in a relationship shownin Table below.

Release Eating behavior Deliciousness evaluation of food RM[0] mechanismWater-drinking Evaluation of distance from RM[1] behavior waterMotivation Eating behavior Hunger Mot[0] creator Water-drinking ThirstMot[1] behavior

Note that in this embodiment, since comparison is made between twooccurrence tendencies of “eating behavior” and “water-drinkingbehavior”, two values RM[0] and RM[1] are selected for the perceptionwhile two values Mot[0] and Mot[1] are taken for the motivation, butcomparison may be made among more occurrence tendencies. Thus, on theassumption that the perception (external intelligent element) is RM[i],motivation (internal motivational element) is Mot[i], occurrencetendency is Be[i] and i is an integer, these items are generalized.These items found in the following description are generalized onesunless specific types of behavior to occur or to be expressed arespecified for them.

In this example, similar occurrence tendencies of “eating behavior” arefound when the cause factors “thirst” and “deliciousness” are in arelation of inverse proportion. For similar degrees of occurrencetendencies, however, the cause factors acting on the occurrence tendencyare not always in such a relation of inverse proportion. Namely, therelation among Be[i], RM[i] and Mot[i] can be given by the followingequation (7) but RM[i] and Mot[i] is not always in aninverse-proportional relation. In short, the occurrence tendency is notinfluenced solely by a motivation (internal motivational element) butalso by a perception (external intelligent element).

Be[i]=func(RM[i], Mot[i])  (7)

Also, the perceptual evaluation RM[i] of “deliciousness” or “distancefrom water” is acquired by the perceptual information acquisition unit90, and motivation Mot[i] like “hunger” or “thirst” is acquired by themotivational information acquisition unit 81. The operations foracquisition of these information by the perceptual and motivationalinformation acquisition units 90 and 81 will be described in detaillater.

The eating and water-drinking tendencies acquired based on theperception (external intelligent element) and motivation (internalmotivational element) as above are as shown in the occurrence tendencyspace in FIG. 14.

In the occurrence tendency space shown in FIG. 14, there are twoswitching liens, first (y=αx) and second (y=βx). That is, the space isdivided into three areas. On the other hand, the occurrence tendencyspace in FIG. 11B has only one switching line. The reason why oneswitching line is set in the space in FIG. 11B while the three switchinglines are set in the space in FIG. 14 is as follows.

Theoretically, different types of behavior can be selected even with oneswitching line as previously described. Should the theory be applied tothe actual robot 1 as it is, however, if the occurrence tendency of eachbehavior is near the switching line, a currently selected behavior andany other behaviors are switched more frequently, making the robot Ithrash between behaviors. Such a phenomenon is caused when theoccurrence tendency of the selected and conducted behavior is smallerthan that of another behavior. Namely, when a motivation (desire) isaccomplished, the degree thereof will be smaller, with the result thatthe occurrence tendency of a behavior influenced by that motivation willbe smaller.

As above, the two switching lines divide the occurrence tendency spaceinto three areas: an area where “eating behavior” is selected (eatingbehavior selecting area), area where “water-drinking behavior” isselected (water-drinking behavior selecting area), and an area whereeither “eating behavior” or “water-drinking behavior” is selected(eating/water-drinking behavior selecting area). Thereby, it is possibleto prevent the robot 1 from thrashing between behaviors. The reason willbe described later why setting of the two switching lines enables therobot 1 to behave evenly.

A behavior showing the strongest occurrence tendency is selected in theoccurrence tendency space shown in FIG. 14 as described below.

As shown in FIG. 14, the occurrence tendency space consists of an eatingtendency Be[0] and water-drinking tendency Be[1] with the eatingtendency Be[0] being taken along the x-axis and the water-drinkingtendency Be[1] being taken along the y-axis. In this occurrence tendencyspace, the first and second switching lines are set as y=αx and y=βx,respectively. For example, coefficients of slopes α and β are arbitraryvalues, and can be decided according to the growth of the robot 1.

The eating tendency Be[0] takes a value based on the “hunger” Mot[0] and“deliciousness” RM[0] shown in FIG. 13A, while the water-drinkingtendency Be[1] takes a value based on the “thirst” Mot[1] and “distancefrom water” RM[1] shown in FIG. 13B.

In the occurrence tendency space, when a value (a, a′) mapped from thecause factor state space lies in the eating behavior selecting area (atpoint C) as shown in FIG. 14, the eating behavior is selected. When thevalue (a, a′) lies in water-drinking behavior selecting area (at pointD), the water-drinking behavior is selected.

The term a of the value (a, a′) is an “eating tendency” Be[0] when the“hunger” is Mot[0]=n₀ and “deliciousness” RM[0]=m₀ as shown in FIG. 13A,while the term a′ of the value (a, b′) is a “water-drinking tendency”Be[1] when the “thirst” Mot[1]=n₁ and “distance from water” RM[1]=m₁ asshown in FIG. 13B.

The above behavior selection can be implemented by the followingalgorithm:

First, a value of a′/a(Be[1]/Be[0]) will be considered for the behaviorselection. It is when ∞>a′/a>β that the value (a, a′) lies in thewater-drinking behavior selecting area defined by x=0 and secondswitching line (y=βx). Also, it is when α>a′/a>0 that the value (a, a′)is in the eating behavior selecting area defined by y=0 and firstswitching line (y=αx).

The following relation can be derived from the above expressions. Whenα>a′/a>0, namely, when the value (a, a′) lies in the eating behaviorselecting area, the occurrence tendency space will be as shown in FIG.15A and the following relations (8) and (9) are established.

aα−a′>0  (8)

1−a′/a>0  (9)

The coefficient of slope α of the first switching line can be given asα′ in a relation to α as given by the equation (10). The value α′ is again (>1) of behavior arbitration(exclusive control) from thewater-drinking tendency Be[1] against eating tendency Be[0] as describedin detail later.

(Be[0])/(Be[1])=1/α=α′  (10)

It will be derived from such a relation that the “eating behavior” isselected when the requirement (11) is met:

a−a′α′>0  (11)

FIG. 15B shows the selection of the water-drinking behavior. The slope βof the second switching line is given by the following equation (12).Note that β is a gain (>1) of behavior arbitration(exclusive control)from the eating tendency Be[0] against the water-drinking tendencyBe[1].

(Be[1])/(Be[0])=β  (12)

It will be derived from the above relation that the “water-drinkingbehavior” is selected when the requirement given by the relation (13) ismet:

a′−aβ>0  (13)

The above requirements are met by the following relations (14) and (15).When the requirement (14) is met, the eating behavior occurs. When therequirement (15) is met, the water-drinking behavior occurs.

a−a′α′>0  (14)

a′−aβ>0  (15)

Expression of the above (a−a′α′) and (a′−aβ) as a matrix will result inthe following equation (16): $\begin{matrix}{\begin{bmatrix}{B\quad {e_{t}\lbrack 0\rbrack}} \\{B\quad {e_{t}\lbrack 1\rbrack}}\end{bmatrix} = {\begin{bmatrix}{B\quad {e_{t}\lbrack 0\rbrack}} \\{B\quad {e_{t}\lbrack 1\rbrack}}\end{bmatrix} - {\begin{bmatrix}0 & \alpha^{\prime} \\\beta & 0\end{bmatrix}\begin{bmatrix}{B\quad {e_{({t - 1})}\lbrack 0\rbrack}} \\{B\quad {e_{({t - 1})}\lbrack 1\rbrack}}\end{bmatrix}}}} & (16)\end{matrix}$

It is assumed that the above equation is calculated discretely. Theabove equation can be expressed with an occurrence tendency Be_(t)[i] ata time t and occurrence tendency Be_((t−1))[i] at a time t−1 as given bythe following equation (17): $\begin{matrix}{\begin{bmatrix}{B\quad {e\lbrack 0\rbrack}} \\{B\quad {e\lbrack 1\rbrack}}\end{bmatrix} = {\begin{bmatrix}a \\a^{\prime}\end{bmatrix} - {\begin{bmatrix}0 & \alpha^{\prime} \\\beta & 0\end{bmatrix}\begin{bmatrix}a \\a^{\prime}\end{bmatrix}}}} & (17)\end{matrix}$

where α′ is a gain (>1) of behavior arbitration(exclusive control) fromthe water-drinking tendency

Be[1] against the eating tendency Be_(t)[0], and β is a gain (>1) ofbehavior arbitration(exclusive control) from the eating tendencyBe_(t)[0] against the water-drinking tendency Be_(t)[1]. For example, itcan be visualized as shown in FIG. 16 that α=works as a gain of behaviorarbitration(exclusive control) against the eating tendency Be_(t)[0]while β works as a gain of behavior arbitration(exclusive control)against the water-drinking tendency Be_(t)[1].

Thus, occurrence tendencies of a set of behaviors can be expressed inthe form of a determinant. When there is a positive Be_(t)[i] in aleft-side matrix of the determinant, a behavior corresponding to theoccurrence tendency Be_(t)[i] is selected.

Note that since in the above determinant, the value of one of theoccurrence tendencies is negative, the equation should be calculatedwith 0 placed for the negative occurrence tendency.

With equation(17) being solved iteratively, the behavior selection ismade as shown in FIG. 17.

It is assumed here that when the selected one of the behaviors isconducted, the cause factors will have less influence on the behaviorand the occurrence tendency of the conducted behavior will be smaller.That is, for example, when “eating behavior” is selected as a behavior,the eating behavior is performed and the motivation etc. for eating isattained, so that the influence of the cause factors (motivation) on the“eating behavior” will be smaller and the eating tendency will besmaller (weaker). The behavior selection is performed by behaviorarbitration(exclusive control) using equation (17) iteratively asdescribed below.

As shown in FIG. 17, for example, when (eating tendency Be[0],water-drinking tendency Be[1])=(a, a′) lies in the eating behaviorselecting area (an area defined by y=0 and y=αx), the eating behaviorwill be selected as a behavior so long as the value (a, a′) lies in theeating behavior selecting area. When the value (a, a′) lies in theeating behavior selecting area, the eating tendency Be_(t)[0] on theleft side of the equation (17) will have a positive value.

As the eating behavior is continuously selected, the influence of theoccurrence of the eating behavior on the cause factors will be smallerso that the eating tendency Be_(t)[0] will be smaller (weaker). When theeating tendency Be_(t) is smaller, the value (a, a′) will reach theeating/water-drinking behavior selecting area. That is, the value (a,a′) will vary as indicated by the arrow P₁ in the graph shown in FIG.17.

In the eating/water-drinking behavior selecting area, the eatingbehavior is selected. The eating tendency Be_(t)[0] on the left side ofthe equation (17) will have a positive value. As the eating behavior iscontinuously selected, the influence of the occurrence of the eatingbehavior on the cause factors will be smaller so that the eatingtendency Be_(t)[0] will be smaller. Then the value (a, a′) will changefrom the eating/water-drinking behavior selecting area to thewater-drinking behavior selecting area (an area defined by x=0 andy=βx). That is, the value (a, a′) will vary as indicated by the arrow P₂in the graph shown in FIG. 17.

In the water-drinking behavior selecting area, the water-drinkingbehavior is selected. When the value (a, a′) lies in water-drinkingbehavior selecting area, the water-drinking tendency Be_(t)[1] on theleft side of the equation (17) will have a positive value at this time.

Then, as the water-drinking behavior is continuously selected, theoccurrence of the water-drinking behavior will have a smaller influenceon the cause factors, the water-drinking tendency Be_(t)[1] will besmaller. Then, the value (a, a′) will go from the water-drinkingbehavior selecting area to the eating/water-drinking behavior selectingarea. In the eating/water-drinking behavior selecting area, thewater-drinking behavior is selected and the water-drinking tendencyBe_(t)[1] on the left side of the equation (17) will have a positivevalue. Further, as the water-drinking behavior is continuously selected,the water-drinking tendency Be_(t)[1] will be smaller so that the value(a, a′) will go from the eating/water-drinking behavior selecting areato the eating behavior selecting area. In the eating behavior selectingarea, the eating behavior is selected again. That is, the change of thevalue (a, a′) from the water-drinking behavior selecting area to theeating behavior selecting area is as indicated by the arrow P₃ in thegraph shown in FIG. 17. The newly selected behavior is then switched tofrom the old one.

Also, providing two switching lines in the occurrence tendency spaceprevents frequent switching by behaviors, and thus the robot 1 can beprevented from thrashing between behaviors.

Since the eating tendency Be_(t)[0] and water-drinking tendencyBe_(t)[1] vary as above, the value (a, a′)=(Be_(t)[0], Be_(t)[1]) isspecified in the occurrence tendency space based on the relation of thevalues of the eating and water-drinking tendencies, whereby a behavioris selected. At this time, either the eating tendency Be_(t)[0] orwater-drinking tendency Be_(t)[1] in equation (17) will have a positivevalue, and the occurrence tendency taking the positive value will be aselected behavior. Such a behavior decision is made by the behaviordecision unit 71 shown in FIG. 5.

Note that in the above, the embodiment of the present invention has beendescribed concerning the example in which two behaviors, eating andwater-drinking, are switched between them based on the eating tendencyBe_(t)[0] and water-drinking tendency Be_(t)[1]. Actually, however, morebehaviors (a number n of behaviors) are compared with each other in theoccurrence tendency space to select one of the behaviors. That is, abehavior is selected in an occurrence tendency space defined by ndimensions. For selection of one of n behaviors, the determinant givenby the equation (18) is available. $\begin{matrix}{\lbrack \quad \begin{matrix}{B\quad {e_{t}\lbrack 0\rbrack}} \\\vdots \\{B\quad {e_{t}\lbrack {n - 1} \rbrack}}\end{matrix} \rbrack = {{\lbrack \quad \begin{matrix}{B\quad {e_{t}\lbrack 0\rbrack}} \\\vdots \\{B\quad {e_{t}\lbrack {n - 1} \rbrack}}\end{matrix} \rbrack \quad\lbrack \quad \begin{matrix}0 & {{G\lbrack 1\rbrack}\lbrack 0\rbrack} & \ldots & {{G\lbrack {n - 1} \rbrack}\lbrack 0\rbrack} \\\vdots & {\vdots \quad} & ⋰ & \vdots \\{{G\lbrack 0\rbrack}\lbrack {n - 1} \rbrack} & {{G\lbrack 1\rbrack}\lbrack {n - 1} \rbrack} & \ldots & 0\end{matrix} \rbrack}\quad\begin{bmatrix}{B\quad {e_{({t - 1})}\lbrack 0\rbrack}} \\\vdots \\{B\quad {e_{({t - 1})}\lbrack {n - 1} \rbrack}}\end{bmatrix}}} & (18)\end{matrix}$

where G[i] and G[j] are gains of behavior arbitration(exclusive control)of occurrence tendency Be_(t)[i] of a behavior against the occurrencetendency Be_(t)[j] of another behavior.

With the above-mentioned algorithm, the occurrence tendency of eachbehavior can be determined based on cause factors such as a perceptionand motivation, and a behavior can be decided using the ethologicalapproach in which a behavior is decided (selected) based on the strength(magnitude) of the occurrence tendency.

Note that when a behavior has been selected as shown in FIG. 17, it maybe considered that the occurrence tendency is finally minimized to 0,namely, the eating tendency Be[0] and water-drinking tendency Be[1] areminimized to 0 (origin), which accounts for the fact that as a behavioris expressed as above, the influence of cause factors (e.g., amotivation) on the behavior approaches zero.

However, there is no problem with the ethological approach-basedbehavior decision since the influence of the cause factors affectingselection of the currently unselected behavior is ongoing. That is,while the water-drinking behavior is being expressed, for example, the“hungry” state being one of the cause factors for the unselected eatingbehavior varies and thus the evaluation of “hunger” varies so that theeating tendency will be higher, which accounts for the fact that“sleeping” or “walking” enables recovery of the appetite. Namely, theoccurrence tendency of an unselected behavior is recovered while theselected behavior is being expressed. This is shown in FIG. 18 forexample.

The slopes α and β of the first and second switching lines can be setarbitrarily. Thus, by setting them adaptively to a stage of growth orcharacter of the robot 1, it is possible to express such a behaviorappropriately.

For example, the robot 1 has a growing behavior model by which differentbehaviors are expressed according to stages of growth. When the stage ofgrowth is “infant”, the slope a of the first switching line and slope βof the second switching line are taken to be near in value to each otherand the eating/water-drinking behavior selecting area is narrowedcorrespondingly. When the stage of growth is “adult”, the slope α of thefirst switching line and slope β of the second switching line are set tosuch values, respectively, that the eating/water-drinking behaviorselecting area is wider.

Thus, when the robot 1 is at the stage of “infant”, frequent switchingis made between the eating behavior and water-drinking behavior and therobot 1 thrashes between behaviors. When the robot 1 is at the stage of“adult”, switching between the eating and water-drinking behaviors ismade at proper intervals and the robot 1 will behave more stably.

Also, the velocity of recovery of the occurrence tendency can be variedaccording to the level of growth. For example, when the level of growthis low, the recovery velocity is set high. When the growth level ishigh, the recovery velocity is set slow. In this case, when the robot 1is at the “infant” stage, switching between the eating andwater-drinking behaviors is made frequently. On the other hand, when therobot 1 is at the “adult” stage, switching between the eating andwater-drinking behaviors is made appropriately. Thus, a similar effectis produced.

Note that occurrence tendencies are prevented from mimimizing to zero bytheir recovery, but the minimization can be prevented also bycomputation.

In the foregoing, the formulae for realization of the ethologicalapproach-based behavior decision in the robot 1 has been described. Thebehavior selection unit 80 selects a behavior using such formulae.

(3-6) Operations in the Behavior Selection Unit 80

The actual operations in the behavior selection unit 80 will bedescribed below.

As shown in FIG. 19, the behavior selection unit 80 includes theperceptual information acquisition unit (release mechanism) 90 toacquire perceptual information (RM), motivational informationacquisition unit (motivation creator) 81 to acquire motivationalinformation (Mot), and a behavior selecting processor 82 to select abehavior based on perceptual information (RM) and motivationalinformation (Mot).

(3-6-1) Procedure for Acquisition of Occurrence Tendency

The procedure for determining an occurrence tendency Be_(t)[i] based onperceptual evaluation (perceptual information) RM[i] and motivationalstate (motivational information) Mot[i] will be descried hereafter. Theprocedure for determination of an occurrence tendency Bet[i] consistsmainly of computation of the value of the occurrence tendency beforebehavior arbitration(exclusive control) and computation of the value ofthe occurrence tendency after behavior arbitration(exclusive control).That is, the procedure for determination of the occurrence tendencyBe_(t)[i] consists mainly of computation of the occurrence tendencyBe_(t)[i] on the right side of the equation (18) and the occurrencetendency Be_(t)[i] on the left side of the equation (18).

The former and latter computations will be described taking, by way ofexample, the acquisition of occurrence tendencies Be_(t)[i] of threedifferent behaviors. The three different behaviors belong to the samebehavior group. As shown in FIG. 20 for example, three perceptualevaluations, first to third, RM[0], RM[1] and RM[2] and threemotivational states, first to third, Mot[0], Mot[1] and Mot[2] are usedto acquire three corresponding occurrence tendencies, first to third,Be_(t)[0], Be_(t)[1] and Be_(t)[2] for three different behaviors,respectively.

The three different behaviors whose tendencies of occurrence are to becompared include “eating behavior”, “water-drinking behavior” and“eliminative behavior”. For the first behavior “eating”, the firstperceptual evaluation RM[0] is “deliciousness” and the firstmotivational state Mot[0] is “hunger”. For the second behavior “waterdrinking”, the second perceptual evaluation RM[1] is “distance fromwater” and the second motivational state Mot[1] is “thirst”. Finally,for the third behavior “elimination”, the third perceptual evaluationRM[2] is “distance from defecation site” and the third motivationalstate Mot[2] is “desire to evacuate the bowels or urinate”. Theoccurrence tendency space consists of these eating tendency Be_(t)[0],water-drinking tendency Be_(t)[1] and elimination tendency Be_(t)[2].

The occurrence tendencies Be_(t)[i] corresponding to the “eatingbehavior”, “water-drinking behavior” and “eliminative behavior”,respectively, based on the perceptual evaluations RM[i] and motivationalstates Mot[i] are computed as described below.

The occurrence tendency Be_(t)[i] is computed using the followingequation (19) from the perceptual evaluation RM[i] and motivationalstate Mot[i]:

Be_(t) [i]=RM[i]×Mot[i]  (19)

When the perceptual evaluation RM[i] and motivational state Mot[i] forman inverse proportional relation, where the relation can be expressed asgiven by the following equation (20):

RM[i]=A[i]/Mot[i]  (20)

When the perceptual evaluation RM[i] is placed in equation (19), thecoefficient A[i] can be acquired as Be_(t)[i]. Namely, in case thatthere is an inverse proportional relation between the perceptualevaluation RM[i] and motivational state Mot[i], the coefficient A[i] iscomputed as the occurrence tendency Be_(t)[i].

With this computation, an occurrence tendency Be_(t)[i] before behaviorarbitration(exclusive control) can be computed. An occurrence tendencyBe_(t)[i] with consideration given to the behavior arbitration(exclusivecontrol) can be computed by the following equation (21): $\begin{matrix}{\begin{bmatrix}{B\quad {e_{t}\lbrack 0\rbrack}} \\{B\quad {e_{t}\lbrack 1\rbrack}} \\{B\quad {e_{t}\lbrack 2\rbrack}}\end{bmatrix} = {\begin{bmatrix}{B\quad {e_{t}\lbrack 0\rbrack}} \\{B\quad {e_{t}\lbrack 1\rbrack}} \\{B\quad {e_{t}\lbrack 2\rbrack}}\end{bmatrix} - {\begin{bmatrix}0 & {{G\lbrack 1\rbrack}\lbrack 0\rbrack} & {{G\lbrack 2\rbrack}\lbrack 0\rbrack} \\{{G\lbrack 0\rbrack}\lbrack 1\rbrack} & 0 & {{G\lbrack 2\rbrack}\lbrack 1\rbrack} \\{{G\lbrack 0\rbrack}\lbrack 2\rbrack} & {{G\lbrack 1\rbrack}\lbrack 2\rbrack} & 0\end{bmatrix}\begin{bmatrix}{B\quad {e_{({t - 1})}\lbrack 0\rbrack}} \\{B\quad {e_{({t - 1})}\lbrack 1\rbrack}} \\{B\quad {e_{({t - 1})}\lbrack 2\rbrack}}\end{bmatrix}}}} & (21)\end{matrix}$

It can be visualized as shown in FIG. 21 that the first, second andthird occurrence tendencies Be_(t)[0], Be_(t)[1] and Be_(t)[2] arecomputed as arbitrated by behavior arbitration(exclusive control) gainsG[i] and G[j] (i=0, 1, 2; j=0, 1, 2).

As above, an occurrence tendency before behavior arbitration(exclusivecontrol) is computed, and an occurrence tendency is computed using theoccurrence tendency before behavior arbitration(exclusive control) andwith consideration given to the behavior arbitration(exclusive control).

A series of these computations is effected by following the procedure asshown in FIG. 22 for example.

First in step S1, each value is initialized with t=0 andBe_((t−1))[i]=0. Then in steps S2 to S6, the value of the first term onthe right side of the equation (21) is computed for Be_(t)[0] toBe_(t)[2]. That is, an occurrence tendency Be_(t)[i] before behaviorarbitration(exclusive control) is computed. The operations in steps S2to S6 are described below.

In step S2, it is assumed that i=0. Thus, the computation of Be_(t)[0]is started.

In the next step S3, perceptual evaluation RM[0] and motivational stateMot[0] are computed. That is, for example the evaluation RM[0] of“deliciousness” and “hungry” state Mot[1] are acquired.

In step S4, an occurrence tendency Be_(t)[0] for “eating behavior” iscomputed as the value of the first term on the right side of theequation (21).

Then in step S5, it is judged whether i=3. More particularly, it isjudged whether the values of all the occurrence tendencies Be_(t)[0] toBe_(t)[2] to be compared have been computed.

When i=3, i is made i=i+1 in step S6 where the operations in step S3 andsubsequent steps are repeated.

With the operations in steps S1 to S6, the water-drinking tendencyBe_(t)[1] and eliminative tendency Be_(t)[2] will be computed as valuesbefore behavior arbitration(exclusive control), next to the eatingtendency Be_(t)[0].

In step S5, when i=3, the operation in step S7 will be performed. Instep S7, the occurrence tendency Be_(t)[i] (i=0 to 2) on the left sideof the equation (21) is computed. That is, an occurrence tendencyBe_(t)[i] with consideration given to behavior arbitration(exclusivecontrol) is computed using the equation (21).

Next in step S8, it is judged whether any one of the tendenciesBe_(t)[i] takes a positive value. When none of the tendencies Be_(t)[i]is positive, the time t is made t=t+1 in step S9 where the operations instep S1 and subsequent steps are repeated. Thus, a iterative computationas given by the equation (21) will be effected. That is, computation ismade using Be_((t−1))[i] in place of the value Be_(t)[i] having beenacquired in the preceding step.

On the other hand, when any of the occurrence tendencies Be_(t)[i] ispositive, a behavior corresponding to that occurrence tendency Be_(t)[i]is selected as the one behavior to actually be expressed, with exit fromthe behavior selection procedure.

As above, the occurrence tendency Be_(t)[i] can be determined based onthe perceptual evaluation (perceptual information) RM[i] andmotivational state (motivational information) Mot[i].

(3-6-2) Operations in the Perceptual Information Acquisition Unit 90

Next, operation of the perceptual information acquisition unit 90 toacquire perceptual evaluation RM[i] and motivational informationacquisition unit 81 to acquire motivational state Mot[i] will bedescribed in detail below. First, the description is started with theperceptual information acquisition unit 90.

In response to external or internal information (recognition result),the perceptual information acquisition unit 90 acquires perceptualinformation (evaluation) being one of the cause factors of a behavior.As shown in FIG. 23, the perceptual information acquisition unit 90includes a behavior memory 91, object name memory 92, object decisionunit 93, object information memory 94 and perceptual informationprocessor 95.

The behavior memory 91 stores a set of selectable memories in a database for example.

Supplied with a behavior group number (signal), the behavior memory 91outputs a set of behaviors whose tendencies of occurrence are to becompared as one behavior group in the object decision unit 93.

For example, “eating an apple (apple eating behavior)” will beconsidered in the following:

The “apple eating” behavior is finally expressed through “approachingthe apple”, “sniffing at the apple”, “taking the apple into the mouth”,“touching the apple”, etc. “Approaching” is a behavior to shorten thedistance from an object, “sniffing” is a behavior to bring the nose, forexample, near to the object, “taking into the mouth” is a behavior totake the object into the mouth, and “touching” is a behavior to bringthe hand (paw; leg) in contact with the object. These “approaching,“sniffing”, “taking into the mouth” and “touching” behaviors can be madeto all common edible objects. For example, when the object is an“orange”, the behavior to shorten the distance from the object is“approaching the orange”, the behavior to bring the nose close to theobject is “sniffing at the orange”, the behavior to take into the mouthis “taking the orange into the mouth”, and the behavior to put the handin contact with the orange is “touching the orange”.

The behavior memory 91 outputs information on a set of behaviors such as“approaching, applicable to all common objects, as one behavior group tothe object decision unit 93. That is, the behavior memory 91 providesthe object decision unit 93 with behavior name information defined byextracting information on an object to which a low-order behavior whichrealizes a high-order behavior such as “eating an apple” is applied. Thebehavior name information output from the behavior memory 91 correspondsto behaviors whose tendencies of occurrence are compared with each otherin the behavior selecting processor 82. Namely, the behavior nameinformation is in a mutually-controlled relation with such behaviorsthemselves.

The behavior name information applicable to all common objects is heldin the behavior memory 91 for the purposes of eliminating the necessityof defining a set of signals (commands) for one behavior applicable todifferent objects, thereby ensuring scalability, which would unavoidablybe in case a behavior is defined for each of objects, in order toprevent any large difference in action from one object to another duringreproduction of a similar behavior. Note that a special behavior shouldbe defined together with information on an object for which the behavioris intended.

On the other hand, the object name memory 92 stores object names. Theobject name stored in the object name memory 92 is a one selected for ahigh-order behavior. For example, when the robot 1 recognizes theexistence of an apple, a high-order behavior “eating an apple (appleeating behavior)” is selected. In this case, “apple” is stored as anobject name into the object name memory 92, and the object name memory92 will output the object name information to the object decision unit93.

The aforementioned behavior memory 91 outputs low-orderbehavior-relevant behavior information applicable to all common objectsto the object decision unit 93. The object name memory 92 will outputone of the object names to the object decision unit 93. Therefore, theobject decision unit 93 will form a set of behaviors whose tendencies ofoccurrence are to be compared with complete information from theinformation output (behavior name signal) from the behavior memory 91and information output (object signal) from the object name memory 92.

The object decision unit 93 outputs a set of behavior information(behavior group signal) in a comparable form to the perceptualinformation processor 95. That is, the object decision unit 93 outputs,to the perceptual information processor 95, a pair of behavior namesincluding one acquired by the behavior memory 91 and the other acquiredby the object name memory 92.

Note that all the set of behaviors whose tendencies of occurrence are tobe compared should not be in conjugation with corresponding objects.Namely, in response to information on a behavior not intended for anyobject, the object name memory 92 will output information “there is nocorresponding object” to the object decision unit 93. The objectdecision unit 93 outputs, to the perceptual information processor 95,behavior information output from the behavior memory 91 as informationon a behavior without corresponding objects.

The behavior memory 91, object name memory 92 and object decision unit93, constructed as above, work as described below. For example, whensupplied with a behavior group number “1”, the behavior memory 91 willoutput “behavior 0”, “behavior 1”, “behavior 2” and “behavior 3”included in the behavior group number “1” to the object decision unit93. On the other hand, the object name memory 92 outputs “food” for the“behavior 0”, “water” for the “behavior 1”, “no object” for the“behavior 2” and “no object” for the “behavior 3”. In this example, thehigh-order behavior is an “ingestive behavior”. When the high-orderbehavior is “eating an apple” as above, the object name memory 92 willoutput only “apple”. Then, the object decision unit 93 will output apair of each “behavior” from the behavior memory 91 and “object name”from the object name memory 92, as significant object information, tothe perceptual information processor 95.

The input semantics converter module 59 outputs, to the objectinformation memory 94, information on a perception supplied to the robot1, and the object information memory 94 stores information on theperception sent from the input semantics converter module 59. Namely,the object information memory 94 stores parameters for perceptionevaluations used for computation of an occurrence tendency, such asobjects “apple”, “distance from the apple”, “direction of the apple”,etc.

Based on the object information (object information signal) from theobject information memory 94 and behavior group information (behaviorgroup information signal) from the object decision unit 93, theperceptual information processor 95 acquires perceptual evaluationsRM[i] for behaviors whose tendencies of occurrence are compared in thebehavior selecting processor 82. That is, for example, “distance fromthe apple” is used for perceptual evaluation of “eating the apple (appleeating behavior)” or Approaching the apple.

Then the perceptual evaluation RM[i] acquired by the perceptualinformation processor 95 is sent to the behavior selecting processor 82.For example, the perceptual evaluation RM[i] is sent as a vectormagnitude from the perceptual information acquisition unit 90 to thebehavior selecting processor 82 as shown in FIG. 19.

Note that a sync signal can be supplied from the object decision unit 93to the object information memory 94. The sync signal can be used toprovide synchronization between the output from the object decision unit93 and that from the object information memory 94, whereby theperceptual information processor 95 can be supplied with a parametercorresponding to a behavior from the object decision unit 93 at adetermined time. Basically, the robot 1 includes only one perceptualinformation acquisition unit 90. However, one perceptual informationacquisition unit 90 may be provided for each of behaviors. In this case,the perceptual information acquisition unit 90 may work withconsideration given only to the application of one behavior to allcommon objects, and thus the behavior memory 91 becomes unnecessary. Inthis example, the behavior selection unit is constructed from a set ofobjects as will be described in detail later.

The operating procedure of the perceptual information acquisition unit90 is described with reference to FIG. 24.

First in step S11, a behavior group name is acquired. The behavior groupincludes the low-order behaviors of “eating an apple”, such as “approachto the apple”, “sniffing at the apple”, etc.

Next, an object selecting routine is executed. Through the objectselecting routine, the behavior name group is acquired in step S12.Thus, a set of behaviors (behavior information in a form applicable toall common objects) is stored in the behavior memory 91. The behaviorinformation defines behavior names such as “approaching, “sniffing”,etc.

In step S13, an object name is acquired. Thus, the acquired behaviorname through the high-order behavior is stored in the object name memory92. The object name is for example “apple”.

The object selecting routine is then executed to acquire a behavior namegroup and object name. Next in step S14, it is judged whether perceptualevaluation RM[i] has been computed for all selected behaviors in theperceptual information processor 95. In case the perceptual evaluationRM[i] has been computed for all the selected behaviors, the procedure isterminated. When the computation of the perceptual evaluation RM[i] isnot complete for all the selected behaviors, a perceptual evaluationcomputing routine is executed.

The perceptual evaluation computing routine is executed in theperceptual information processor 95 and consists of the following steps.

In step S15, it is judged whether there exists an object. When thejudgment is that an object exists, the procedure goes to step S16. Onthe other hand, if the judgment is “No”, the procedure goes to step S18.

In step S16, the perceptual information processor 95 will acquire adistance and direction of the object (parameters for acquisition ofperceptual evaluation) from the object information memory 94, andcomputes a perceptual evaluation (value) RM[i] in step S17. Namely, forexample, an evaluation RM[i] of “approach to the apple” is computed from“distance from the apple”. Note that the distance is detected by thedistance sensor 22 while the direction is detected using an imagesupplied from the CCD camera 20 or the like.

On the other hand, in step S18, the perceptual information processor 95computes a perceptual evaluation (value) RM[i] without any object. Thisoperation is applicable to a behavior to be evaluated and not intendedfor any object.

The perceptual evaluation computing routine is executed until it isjudged in step S14 that the perceptual evaluation RM[i] has beencomputed for all the behaviors whose tendencies of occurrence are to becompared (a set of behaviors included in the behavior group). That is,with the operations in step S14 and perceptual evaluation computingroutine, perceptual evaluation RM[i] is computed for all the behaviorsincluded in the behavior group.

When it is judged in step S14 that the perceptual evaluation RM[i] hasbeen computed for all the behaviors included in the behavior group, theprocedure is terminated.

The perceptual information acquisition unit 90 operates as above. Withthe perceptual information acquisition unit 90, it is possible toacquire perceptual evaluation RM[i] for a set of behaviors in thebehavior group, whose occurrence tendencies are to be compared.

(3-6-3) Operations in the Motivational Information Acquisition Unit 81

The motivational information acquisition unit 81 acquires a motivationbeing one of the cause factors of a behavior based on the states ofinstinct and emotion, varying adaptively to external or internalinformation (recognition result). The motivational informationacquisition unit 81 has a set of instinct/emotion parameters IE[p](instinct/emotion parameter group) as shown in FIG. 25, and acquires aset of motivations Mot[i] for a behavior. More specifically, themotivation for a behavior is acquired as described below.

The instinct/emotion parameter group IE[p] consists of information whichcan be influenced by instinct and emotion. More particularly, itconsists of a set of parameters determined by the aforementionedinternal state model. Namely, the instinct/emotion parameters includefor example “fatigue”, “temperature”, “pain”, “hunger”, “thirst”,“affection”, “submission”, “curiosity”, “elimination”, “happiness”,“sadness”, “anger”, “surprise”, “disgust”, “fear”, “frustration”,“boredom”, “somnolence”, “gregariousness”, “patience”, “tense/relaxed”,“alertness”, “guilt”, “spite”, “loyalty”, “sexual”, “jealousy”, etc.

The behavior motivation group Mot[i] corresponds to a set of behaviorsincluded in the same behavior group. For example, such behaviors include“hunger” etc. for “ingestive behavior” and “thirst” etc. for“water-drinking behavior”.

The motivational information acquisition unit 81 maps theinstinct/emotion parameters IE[p] to compute a motivation Mot[i] foreach of the behaviors using the following equation (22). $\begin{matrix}{\begin{bmatrix}{M\quad o\quad {t\lbrack 0\rbrack}} \\{M\quad o\quad {t\lbrack 1\rbrack}} \\\vdots \\{M\quad o\quad {t\lbrack 2\rbrack}}\end{bmatrix} = {\begin{bmatrix}{{K\lbrack 0\rbrack}\lbrack 0\rbrack} & {{K\lbrack 0\rbrack}\lbrack 1\rbrack} & \quad & {{K\lbrack 0\rbrack}\lbrack m\rbrack} \\{{K\lbrack 1\rbrack}\lbrack 0\rbrack} & {{K\lbrack 1\rbrack}\lbrack 1\rbrack} & \quad & {{K\lbrack 1\rbrack}\lbrack m\rbrack} \\\vdots & \vdots & \quad & \vdots \\{{K\lbrack i\rbrack}\lbrack 0\rbrack} & {{K\lbrack i\rbrack}\lbrack 1\rbrack} & \quad & {{K\lbrack i\rbrack}\lbrack m\rbrack}\end{bmatrix}\quad\begin{bmatrix}{I\quad {E\lbrack 0\rbrack}} \\{I\quad {E\lbrack 1\rbrack}} \\\vdots \\{I\quad {E\lbrack m\rbrack}}\end{bmatrix}}} & (22)\end{matrix}$

The equation (22) is used to multiply the instinct/emotion parameterIE[p] by a coefficient K[i][p] to compute a motivation Mot[i] for eachof the behaviors by mapping as a linear sum. The motivation Mot[i]computed as determinant is sent as a vector magnitude from themotivational information acquisition unit 81 to the behavior selectingprocessor 82 as shown in FIG. 19.

Taking motivations for “investigative”, “demanding” and “resting”behaviors by way of example, the robot behaviors will be describedlater. The motivation Mot[0] for the “investigation” behavior, Mot[1]for “demanding” behavior and Mot[2] for the “resting” behavior are givenby the following equation (23): $\begin{matrix}{{M\quad o\quad {t\lbrack i\rbrack}} = \begin{bmatrix}{Invetigative} \\{Demanding} \\{Resting}\end{bmatrix}} & (23)\end{matrix}$

Also, K[i][p] is given by the equation (24): $\begin{matrix}{{{K\lbrack i\rbrack}\lbrack p\rbrack} = \begin{bmatrix}{- 10} & 10 & 0 \\0 & 0 & 15 \\10 & {- 5} & 0\end{bmatrix}} & (24)\end{matrix}$

Also, the instinct/emotion parameter IE[p] is given by the followingequation $\begin{matrix}{{I\quad {E\lbrack p\rbrack}} = \begin{bmatrix}{Fatigue} \\{Curiosity} \\{Affection}\end{bmatrix}} & (25)\end{matrix}$

Thus, the motivations for the “investigative”, “demanding” and “resting”behaviors are given by the equation (26): $\begin{matrix}{\begin{bmatrix}{Invetigative} \\{Demanding} \\{Resting}\end{bmatrix} = {\begin{bmatrix}{- 10} & 10 & 0 \\0 & 0 & 15 \\10 & {- 5} & 0\end{bmatrix} \times \begin{bmatrix}{Fatigue} \\{Curiosity} \\{Affection}\end{bmatrix}}} & (26)\end{matrix}$

In the equation (26), “investigation” is a function of aninstinct/emotion parameter in which “fatigue” acts as a negative factorwhile “curiosity” acts as a positive factor. Also, the “demanding” is afunction of an instinct/emotion parameter in which “affection” acts as apositive factor. “Resting” is a function of an instinct/emotionparameter in which “fatigue” acts as a positive factor while “curiosity”acts as a negative factor.

The first example in which the instinct/emotion parameter IE[p] is [10,50, 20] will be considered here. In this state, the curiosity is high.The “investigative” Mot[0] is 400 (=−100+500+0), “demanding” Mot[1] is300 (=0+0+300), and “resting” Mot[2] is −150 (=−100−250+0).

Next, a second example in which the instinct/emotion parameter IE[p] is[70, 10, 30] will be considered. This state means that the robot isfatigued by the investigation. In this state, the “investigation” Mot[0]is −600 (=−700+100+0), “demanding” Mot[1] is 450 (=0+0+450), and“resting” Mot[2] is 650 (=700−50+0).

A third example in which the instinct/emotion parameter IE[p] is [30,20, 60] will be considered. In this state, the fatigue has been reducedsomehow and the affection is high. The “investigation” Mot[0] is −100(=−300+200+0), “demanding” Mot[1] is 300 (=0+0+300), and “resting”Mot[2] is 200 (=300−100+0).

As above, a behavior motivation Mot[i] can be acquired based on theinstinct/emotion parameter group IE[p] and coefficient K[i][m]. Byappropriately mapping the instinct/emotion parameter group K[i][p], itis possible to acquire a desired motivation Mot[i] for acquisition ofthe tendency for occurrence RM[i]. That is, motivations such as“thirsty” and “hungry” as above can also be acquired.

The motivational information acquisition unit 81 operates as above. Foreach behavior, a motivation Mot[i] can be acquired by the motivationalinformation acquisition unit 81. The motivation acquired by themotivational information acquisition unit 81 is variable based on theparametric values of the instinct and emotion, and as a result, themotivation will be reflected in a selected behavior. For example in theabove example, the behavior reflects the motivation.

Basically, the desire increases with time elapsed. Therefore, it willcontinuously increase unless gratified. When the curiosity becomes high,the robot 1 will start an investigation (as above first example). As therobot 1 walks around during the investigation, the fatigue will increasecorrespondingly. The curiosity itself will decrease along with theinvestigation. If no information is supplied to the robot 1 even afterwalking for a while, the curiosity decreases and fatigue increases sothat the behavior of the robot 1 is switched to “resting” (as abovesecond example). After some resting, the fatigue is decreased while theaffection increased with time elapsed, and the behavior of the robot 1is switched to “demanding” (as above third example). Thus, themotivation will be reflected in a selected behavior.

Note that the value of the aforementioned coefficient K[i][p] may be setarbitrarily. With the coefficient K[i][p] arbitrarily set, mapping bythe instinct/emotion parameter IE[p] for acquisition of a motivationMot[i] can be varied widely. With the coefficient K[i][p] thus set,mapping can be made in accordance with the kind and growth level of ananimal applied to the robot 1.

In the foregoing, there has been described in detail the perceptualinformation acquisition unit 90 to acquire a perceptual evaluation RM[i]and motivational information acquisition unit 81 to acquire amotivational state Mot[i]. Based on a perceptual evaluation RM[i] andmotivational state Mot[i] acquired by the perceptual informationacquisition unit 90 and motivational information acquisition unit 81,respectively, the behavior selecting processor 82 selects one of thebehaviors.

The above behavior selection is done until a behavior in the lowestbehavior layer is selected. That is, the behavior selection system isconstructed in the form of a hierarchy as shown in FIG. 7. The behaviorselection with the perceptual evaluation RM[i] and motivationalinformation Mot[i] is effected in each layer as mentioned above untilone of the behaviors in the lowest layer (behavior to actually beoutput) is decided. Namely, as shown in FIG. 6B, the “ingestivebehavior” is the result of the selection made in the subsystem layerbased on perceptual evaluation RM[i] and motivational informationMot[i], “water-drinking behavior” is the result of the selection made inthe mode layer consisting of a group of further realized behaviors basedon the perceptual evaluation RM[i] and motivational information Mot[i],“approach to water” is the result of the selection made in the modulelayer consisting of a group of further realized behaviors based on theperceptual evaluation RM[i] and motivational information Mot[i], and“move forward(advance)” is the result of the selection made in the motorcommand layer consisting of a group of further realized behaviors basedon the perceptual evaluation RM[i] and motivational information Mot[i].With these operations, the “eating behavior” being an abstract behavior(as a desire) is realized by actual behaviors such as “move forward”etc.

Note that for selection of a behavior in each layer, the occurrencetendency of the behavior is computed based on cause factors such asperception and motivation and the behavior is selected based on theresult of computation but motivational information used for computationof the tendency for occurrence of the behavior may be common to all thelayers. That is, for example when the “ingestive behavior” is ahigh-order behavior, all behaviors subordinate to the high-orderbehavior are intended for realization of the “ingestive behavior”. Withthis fact taken in consideration, the low-order behaviors are intendedto appease the “hunger(thirst)”. Therefore, for the low-order behaviorsrealizing the “ingestive behavior”, the “hunger(thirst)” is motivationalinformation (cause factor).

Note that the above is not always true for the perception. This is dueto the fact that the perceptual information (external intelligentelements) for “approach to water” includes “distance from water” but“direction of water” is the most suitable as perceptual information for“move forward(advance)” subordinate to “approach to water” in somecases.

(3-7) Operations in the Modulator 72

The modulator 72 and the output semantics converter module 68 which willbe described later operate to actually express a behavior selected bythe behavior selecting processor 82 as above.

The modulator 72 decides a behavior to finally be expressed based on abehavior selected by the behavior selection unit 80 and representativeemotional information (representative emotional signal) received fromthe internal-state model unit 71.

The representative emotional information output from the internal-statemodel unit 71 indicates the current emotional state of the robot 1. Forexample, the internal-state model unit 71 outputs an instinct (desire)or emotion whose parametric value is the largest of the representativeemotional information.

The modulator 72 modulates a behavior selected by the behavior selectionunit 80 based on the above representative emotion. Namely, the modulator72 works to express an emotion by a behavior.

As above, it is not necessary to directly express a current emotion as abehavior of the robot 1 but the above procedure is effective forexpression of an emotional behavior. That is, in case the robot 1 is notreally angry but only a little angry, a behavior selected by thebehavior selection unit 80 is accompanied with some “disgust”.

The modulator 72 outputs information on a behavior selected andmodulated with the above emotion to the output semantics convertermodule 68. For example, the modulator 72 outputs behavior information asan abstract-behavior command to the output semantics converter module68.

The output semantics converter module 68 supplies the signal processingmodules 61 to 67 with the output corresponding to the behaviorinformation from the modulator 72. Thus, the robot 1 will output, as anactual behavior, the behavior decided by the behavior decision system70.

The behavior decision system 70 has been described in the foregoing.Owing to this behavior decision system 70, the internal-state model unit71 can change the internal state such as instinct and emotional statesof the robot 1 based on the recognition result from the input semanticsconverter module 59. Also, the behavior selection unit 80 can select abehavior to be expressed by the robot 1 from a set of behaviors based onthe recognition result from the input semantics converter module 59.

Then the modulator 72 generates behavior information having emotionadded thereto based on the internal state acquired by the internal-statemodel unit 71 and behavior acquired by the behavior selection unit 80,and outputs the behavior information with the emotion to the outputsemantics converter module 68.

(4) Operations of the Output Semantics Converter Module 68

The output semantics converter module 68 holds information on the type(biped or quadruped), shape, etc. of the robot 1, and controls thesignal processing modules 61 to 67 to realize behavior information fromthe modulator 72 according to the information related to the robot 1. Incase the robot 1 is of quadruped type for example, since the outputsemantics converter module 68 knows that the robot 1 is of the quadrupedtype, when it is supplied with behavior information “advance(moveforward)” from the modulator 72, it outputs a command to the signalprocessing modules which control the four legs in order to realize thebehavior “advance”. At this time, receiving the abstract behaviorcommands from the modulator 72, the output semantics converter module 68will send a command to each of the signal processing modules 61 to 67which control the four legs.

The signal processing modules 61 to 67 control the correspondingdevice-based commands from the output semantics converter module 68.Thus, a behavior decided (selected) in the aforementioned behaviordecision system 70 will be expressed as an actual behavior of the robot1.

Also, the robot 1 behaves with the posture and motion thereof beingmanaged. Each component of the robot 1 works independently in principlebut since the posture and motion are thus managed, the robot 1 isinhibited from making a predetermined motion independently.

As shown in FIG. 1, the robot 1 includes the body unit 2 and the legunits 3A to 3D, head unit 4 and tail unit 5, all coupled to the bodyunit 2. Thus, basically in the robot 1, these units can moveindependently for a selected behavior under the control of the signalprocessing modules 61 to 67. However, the interference between the unitswill result in appropriate motion in some cases. Also, transition from acurrent posture to an intended posture or motion is impossible incertain cases.

To prevent any unreasonable or impossible posture or such theinterference between the units, the units are aligned with each other tomanage the posture and motion. In the robot 1, the posture and motionare managed by the signal processing module (posture management module)61 shown in FIG. 4.

More particularly, when the posture management module 61 is suppliedwith an instruction “move forward(advance)” while the robot 1 is in thesitting position, search is made for a posture changing path along whichthe posture is changed from “sitting” to “walking” state. For example,search is made for a posture changing path along which the posture ischanged from “sitting” to “walking” state through a set of postures andmotions. Then, based on the result of the search for the posturechanging path from the “sitting” to “walking” state, instructions aresent to the signal processing modules according to the order of thechanging paths in order to realize a posture and motion on the changingpath. Thus, in the robot 1, a desired target posture and motion, namely,a behavior having been decided by the aforementioned behavior decisionsystem 70, can be realized with the prevention of any impossible orunreasonable posture or interference between the units.

The construction and operations of the robot 1 have been described inthe foregoing. Owing to the aforementioned construction, the robot 1 canoutput a behavior decided using the ethological approach. Thus, therobot 1 will have an improved likeness to a living thing or a creatureand the user will feel more familiar and satisfactory with the robot 1.

(5) Other Modes for Carrying out the Present Invention

In the foregoing, the best mode for carrying out the present inventionhas been described concerning the robot 1. However, the presentinvention can be embodied in any of the modes which will be describedbelow.

In the aforementioned embodiment, the behavior decision system 70finally decides to select a behavior with reference to motivationalinformation. For example, in the example shown in FIG. 6B, the behavior“move forward(advance)” is selected with reference to motivationalinformation. However, a final behavior selection may be decided withoutreference to such motivational information.

More specifically, as shown in FIGS. 26A and 26B for example, a behavior“approach to water” subordinate to “ingestive behavior” and further abehavior “move forward” subordinate to the behavior “approach to water”are selected with reference to information (except for motivationalinformation), such as perceptual information such as distance from theobject in consideration. For example, when a certain behavior(indeterminate behavior) is intended, a motivation will greatly act onthe selection of the behavior. The behavior range including possiblebehaviors is narrowed and then with the possible behaviors beingdissociated from the motivation, the behavior selecting procedure (thebehavior selecting thought) is switched to one for realization of thebehaviors. Namely, a behavior finally selected is decided so as not tobe influenced by the motivation. Then, for example, perceptualinformation is used for final decision of the behavior. Note that it maybe defined that the mode layer is the 0-th layer while the module layeris the first layer.

For example, to decide a behavior not based on motivational informationas above, a motion generator 100 is provided as shown in FIGS. 27 and28. The motion generator 100 selects a behavior “approach to water” anda motion subordinate to the former behavior and which will realize amotion “move forward(advance)”, from behaviors selected by the behaviorselecting processor based on perceptual information etc. Then, themotion generator 100 outputs the selected motion to the modulator 72which will output a behavior modulated with an emotion sent from theinternal-state model unit 71 as above.

More specifically, the motion generator 100 includes a perceptualinformation acquisition unit 90 and a behavior selecting processor 102as shown in FIG. 29. For example, an object information memory 94 alsoprovided in the motion generator 100 to store various kinds ofinformation from the output semantics converter module 68 is used toselect a motion in the behavior selecting processor 102. Thus, when theintended behavior is “move forward(advance)”, the motion generator 100uses only information such as a distance from the object (e.g,information that the distance to the object is 10 cm) and the directionto the object (e.g., information that the object is to the right of therobot 1), both elements of information stored in the object informationmemory 94, to select a motion in the behavior selecting processor 102.

In the aforementioned embodiment, one of a set of behaviors is selectedby the behavior selection unit 80. For example, the behavior selectionunit 80 holds information on a set of behaviors and decides one of thebehaviors based on the data on the behaviors. However, the presentinvention is not limited to this manner of behavior decision.

For example, the behavior decision system 70 can have the behaviordecision part thereof designed as object-oriented. Note that even whenthe behavior decision system is built as objected-oriented, thehierarchical structure consisting of high-order behaviors and low-orderbehaviors is used as is. For selection of a behavior, a behavior isselected in units of object from the behavior group organized in unitsof object. More specifically, the behavior decision system has ahierarchical structure consisting of a set of behavior selection units(object or thread) 80 ₁, 80 ₂ and 80 ₃ for selection of a behavior asshown in FIG. 30.

In this embodiment, the behavior selection units as objects are providedin two layers, upper and lower, as shown in FIG. 30. Needless to say,however, the present invention is not limited to this configuration ofthe behavior decision system 70.

Each of the behavior selection units 80 ₁, 80 ₂ and 80 ₃ includes aperceptual information acquisition unit 90, motivational informationacquisition unit 81 and behavior selection processor 82 similar to thebehavior selection unit 80 in the behavior decision system 70.

In this case, based on a behavior selected by the behavior selectionunit 80 ₁ in the higher layer, the behavior selection units 80 ₂ and 80₃ in the lower layer select a behavior. Namely, the behavior selectionin the upper layer leads to the behavior selection by one of thebehavior selection units in the lower layer. The behavior selection unitin the lower layer will select a lower-order behavior.

Then, the behavior selection unit located in the lowest layer of such abehavior decision system consisting of the set of behavior selectionunits 801, 802 and 803 will deliver information on the selected behaviorto the aforementioned motion generator 100.

With the behavior decision system intended for the object-orientedbehavior decision, it is not necessary to always know how the entiresystem stands regarding the behavior decision, whereby the burden of thebehavior decision can be reduced. For the addition of a new behavior, itsuffices to add a corresponding new object, whereby it is madeunnecessary to rewrite all the data for selection of behaviors. Theaddition of a new behavior means acquisition of a new behavior bylearning or addition of a new behavior, incidental to a change of thegrowth level.

The behavior group configurations of the subsystem layer (SUBSYSTEM),mode layers (MODE1 and MODE2) and module layer (MODULE) shown in FIG. 6Bor 26B are shown in further detail in FIGS. 31 and 32. IndustrialApplicability

In the robot apparatus having been described in the foregoing, externalor internal information is detected by a detecting-means, a cause factorinfluencing the behavior is acquired by a cause factor acquiring meansfrom the external or internal information detected by thedetecting-means, an occurrence tendency of the cause factor-influencedbehavior is acquired by an occurrence tendency acquiring means based onthe cause factor acquired by the cause factor acquiring means, acomparison is made by an occurrence tendency comparing means amongoccurrence tendencies of two or more behaviors, acquired by theoccurrence tendency acquiring means and belonging to the same group, oneof the behaviors is selected by a behavior selecting means based on theresult of the occurrence tendency comparison made by the occurrencetendency comparing means, and the moving parts of the robot apparatusare controlled by moving part controlling means based on the behaviorselected by the behavior selecting means to have the robot apparatusexpress the selected behavior. Briefly, the robot apparatus selects oneof the behaviors through a comparison between occurrence tendencies ofthe behaviors, decided under the influence of the cause factor, andexpresses the behavior as an ethological approach.

In the aforementioned method for deciding the behavior of a robotapparatus, external or internal information is detected by adetecting-means in an information detecting step, a cause factorinfluencing the behavior of the robot apparatus is acquired in a causefactor acquiring step from the external or internal information detectedin the information detecting step, an occurrence tendency of the causefactor-influenced behavior is acquired in an occurrence tendencyacquiring step based on the cause factor acquired in the cause factoracquiring step, a comparison is made in an occurrence tendency comparingstep among occurrence tendencies of two or more behaviors, acquired inthe occurrence tendency acquiring step and belonging to the same group,one of the behaviors is selected in a behavior selecting step based onthe result of the occurrence tendency comparison made in the occurrencetendency comparing step, and the moving parts of the robot apparatus arecontrolled in a moving part controlling step based on the behaviorselected in the behavior selecting step to have the robot apparatusexpress the selected behavior. Briefly, the robot apparatus selects oneof the behaviors through a comparison between occurrence tendencies ofthe behaviors, decided under the influence of the cause factor, andexpresses the behavior as an ethological approach.

What is claimed is:
 1. A robot apparatus whose moving parts arecontrolled to make the robotic device behave expressively, the devicecomprising: means for detecting external or internal information; meansfor acquiring a cause factor influencing the behavior from the externalor internal information detected by the information detecting-means;means for acquiring an occurrence tendency of the causefactor-influenced behavior based on the cause factor acquired by thecause factor acquiring means; means for making a comparison amongoccurrence tendencies of two or more behaviors, acquired by theoccurrence tendency acquiring means and belonging to the same group;means for selecting one of the behaviors based on the result of theoccurrence tendency comparison made by the occurrence tendency comparingmeans; and means for controlling the moving parts based on the behaviorselected by the behavior selecting means to have the robot apparatusexpress the selected behavior; wherein the occurrence tendency of thebehavior selected by the behavior selecting means being variedadaptively to the cause factor which is variable due to the actualoccurrence of the behavior; and wherein a set of behaviors are organizedin the form of a hierarchical structure such that a number of low-orderbehaviors are associated with a high-order behavior.
 2. The device ofclaim 1, wherein the cause factor acquiring means acquires at least acause factor concerning the perception and a cause factor concerning themotivation.
 3. The device of claim 2, wherein the cause factor acquiringmeans acquires the cause factor concerning the motivation, consisting ofinstinctive or emotional elements.
 4. The device of claim 3, wherein theinstinctive elements includes at least one of “fatigue”, “temperature”,“pain”, “hunger”, “thirst”, “affection”, “curiosity”, “elimination” and“sexual”, and the emotional elements include at least one of“happiness”, “sadness”, “anger”, “surprise”, “disgust”, “fear”,“frustration”, “boredom”, “somnolence”, “gregariousness”, “patience”,“tense”, “relaxed”, “alertness”, “guilt”, “spite”, “loyalty”,“submission”and “jealousy”.
 5. A robot apparatus whose moving parts arecontrolled to make the robotic device behave expressively, the devicecomprising: means for detecting external or internal information; meansfor acquiring a cause factor influencing the behavior from the externalor internal information detected by the information detecting-means;means for acquiring an occurrence tendency of the causefactor-influenced behavior based on the cause factor acquired by thecause factor acquiring means; means for making a comparison amongoccurrence tendencies of two or more behaviors, acquired by theoccurrence tendency acquiring means and belonging to the same group;means for selecting one of the behaviors based on the result of theoccurrence tendency comparison made by the occurrence tendency comparingmeans; means for controlling the moving parts based on the behaviorselected by the behavior selecting means to have the robot apparatusexpress the selected behavior; wherein the occurrence tendency of thebehavior selected by the behavior selecting means is varied adaptivelyto the cause factor which is variable due to the actual occurrence ofthe behavior; a behavior selection system in which a set of behaviorsare organized in the form of a hierarchical structure, a set oflow-order behaviors belonging to the same group indicating concretebehaviors of high-order behavior, and wherein: the occurrence tendencycomparing means compares occurrence tendencies of the set of low-orderbehaviors in the group corresponding to the high-order behavior; thebehavior selecting means selects a low-order behavior based on theresult of the occurrence tendency comparison by the occurrence tendencycomparing means; and when the behavior selected by the behaviorselecting means is a lowest-order one, the moving part controlling meanscontrols the moving parts based on the lowest-order behavior.
 6. Thedevice of claim 5, wherein: the cause factor acquiring means acquires acause factor concerning the perception and a cause factor concerning themotivation; and the occurrence tendency acquiring means acquires theoccurrence tendency of at least one behavior in the lowest layer basedon the cause factor concerning the perception.
 7. The device of claim 1,further comprising a set of objects for the behavior selection; andwherein the cause factor acquiring means, occurrence tendency acquiringmeans and behavior selecting means are implemented by the objects,respectively.
 8. The device of claim 1, wherein the occurrence tendencycomparing means compares a set of occurrence tendencies by behaviorarbitration(exclusive control) between the occurrence tendencies ofbehaviors whose tendencies of occurrence are to be compared.
 9. Thedevice of claim 1, wherein the detecting-means is a sensor.
 10. Thedevice of claim 9, wherein the cause factor acquiring means acquires acause factor for evaluation of a behavior from sensor information beingexternal or internal information detected by the sensor.
 11. A methodfor deciding the behavior of a robot apparatus whose moving parts arecontrolled to have the robot apparatus behave expressively, the methodcomprising the steps of: detecting external or internal information ofthe robot by an information detecting-means; acquiring a cause factorinfluencing the behavior from the external or internal informationdetected in the information detecting step; acquiring an occurrencetendency of the cause factor-influenced behavior based on the causefactor acquired in the cause factor acquiring step; making a comparisonamong occurrence tendencies of two or more behaviors, acquired in theoccurrence tendency acquiring step and belonging to the same group;selecting one of the behaviors based on the result of the occurrencetendency comparison made in the occurrence tendency comparing step; andcontrolling the moving parts based on the behavior selected in thebehavior selecting step to have the robot apparatus express the selectedbehavior; wherein the occurrence tendency of the behavior selected inthe behavior selecting step being varied adaptively to the cause factorwhich is variable due to the actual occurrence of the behavior; andwherein a set of behaviors are organized in the form of a hierarchicalstructure such that a number of low-order behaviors are associated witha high-order behavior.
 12. The method of claim 11, wherein in the causefactor acquiring step, there are acquired at least one cause factorconcerning the perception and a cause factor concerning the motivation.13. The method of claim 12, wherein in the cause factor acquiring step,there is acquired the cause factor concerning the motivation, consistingof instinctive or emotional elements.
 14. The device of claim 13,wherein the instinctive elements includes at least one of “fatigue”,“temperature”, “pain”, “hunger”, “thirst”, “affection”, “curiosity”,“elimination”and “sexual”, and the emotional elements include at leastone of “happiness”, “sadness”, “anger”, “surprise”, “disgust”, “fear”,“frustration”, “boredom”, “somnolence”, “gregariousness”, “patience”,“tense”, “relaxed”, “alertness”, “guilt”, “spite”, “loyalty”,“submission”and “jealousy”.
 15. The device of claim 11, wherein: therobot apparatus further comprises a set of objects for the behaviorselection; and the cause factor acquiring step, occurrence tendencyacquiring step and behavior selecting step are executed by the objects,respectively.
 16. The device of claim 11, wherein in the occurrencetendency comparing step, a set of occurrence tendencies is compared bybehavior arbitration(exclusive control) between the occurrencetendencies of behaviors whose tendencies of occurrence are to becompared.
 17. The device of claim 11, wherein the detecting-means is asensor.
 18. The device of claim 17, wherein in the cause factoracquiring step, there is acquired a cause factor for evaluation of abehavior from sensor information being external or internal informationdetected by the sensor.
 19. A method for deciding the behavior of arobot apparatus whose moving parts are controlled to have the robotapparatus behave expressively, the method comprising the steps of:detecting external or internal information of the robot by aninformation detecting-means; acquiring a cause factor influencing thebehavior from the external or internal information detected in theinformation detecting step; acquiring an occurrence tendency of thecause factor-influenced behavior based on the cause factor acquired inthe cause factor acquiring step; making a comparison among occurrencetendencies of two or more behaviors, acquired in the occurrence tendencyacquiring step and belonging to the same group; selecting one of thebehaviors based on the result of the occurrence tendency comparison madein the occurrence tendency comparing step; controlling the moving partsbased on the behavior selected in the behavior selecting step to havethe robot apparatus express the selected behavior; the occurrencetendency of the behavior selected in the behavior selecting step beingvaried adaptively to the cause factor which is variable due to theactual occurrence of the behavior wherein the robot apparatus furthercomprises a behavior selection system in which a set of behaviors areorganized in the form of a hierarchical structure, a set of low-orderbehaviors belonging to the same group indicating concrete behaviors ofhigh-order behavior; wherein in the occurrence tendency comparing steps,there are compared occurrence tendencies of the set of low-orderbehaviors in the group corresponding to the high-order behavior; whereinin the behavior selecting step, there is selected a low-order behaviorbased on the result of the occurrence tendency comparison made in theoccurrence tendency comparing step; and wherein in the moving partcontrolling step, when the behavior selected in the behavior selectingstep is a lowest-order one, the moving parts is controlled based on thelowest-order behavior.
 20. The device of claim 19, wherein: in the causefactor acquiring step, there is acquired a cause factor concerning theperception and a cause factor concerning the motivation; and in theoccurrence tendency acquiring step, there is acquired the occurrencetendency of at least one behavior in the lowest layer based on the causefactor concerning the perception.